- Email: [email protected]
A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources
Accepted Manuscript A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources Hithu Anand, Rengaraj Ramasubbu PII:
S0960-1481(18)30535-4
DOI:
10.1016/j.renene.2018.05.016
Reference:
RENE 10068
To appear in:
Renewable Energy
Received Date: 9 September 2016 Revised Date:
1 April 2018
Accepted Date: 3 May 2018
Please cite this article as: Anand H, Ramasubbu R, A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources, Renewable Energy (2018), doi: 10.1016/j.renene.2018.05.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
4
Hithu Ananda , Rengaraj Ramasubbub
6 7 8
9
a Department
of Electrical and Electronics Engineering, Agni College of TechnologyChennai 600 130, Tamil Nadu, India. b Department of Electrical and Electronics Engineering, SSN College of EngineeringChennai 603 110, Tamil Nadu, India.
SC
5
Abstract
M AN U
2
RI PT
3
A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources
1
10
Keywords: Renewable energy sources, micro-grid, real time pricing, dynamic economic emission dispatch, anti-predatory particle swarm optimisation
AC C
11
EP
TE
D
Micro-grid is a complete energy solution for remote areas facing energy crisis however, energy cost from fuel based sources in a micro-grid is higher than conventional sources. So price-signal based control strategy for the benefit of utility and customers by virtue of renewable integration is analysed. Renewable energy has become less costiler than fuel based sources in a micro-grid. Stochastic power generated from wind turbine (WT) and photo voltaic (PV) along with economic generation from micro-turbine (MT) and fuel cell (FC) is optimised. Anti-predatory particle swarm optimisation (APSO) for non-linear dynamic economic emission dispatch of micro-grid for 24 hours with and without renewable energy sources (RESs) is analysed. APSO method with RESs is identified to give better fuel-cost-minimum. Power-to-cost index is proposed as a simple real time pricing (RTP) strategy. The strategy is identified to have the potential to bring down power demand and peak reduction followed by additional reduction in emission and fuel cost in any micro-grid.
12
13
14 15 16 17 18 19 20 21
1. Introduction Technology is advancing at a higher rate, weather forecast is much accurate than before. Years of acquired wind and solar data has brought prediction closer to real time events. Hence the advancement in technology ensure that, installation of renewable energy sources (RESs) are a risk free investment. The rate of installation of RESs, such as wind turbine (WT) and PV has increased. In India, when comparing to official reports from [1] and [2], a 30% capacity increment of renewable energy is observed for a span of less than a year. PV and WT can be installed in areas with consistent irradiation and wind velocity profile. Remote areas without grid connectivity is benefited with RESs. However,
Email address: [email protected] (Hithu Anand) Preprint submitted to Renewable Energy URL: www.ssn.edu.in ()
May 4, 2018
ACCEPTED MANUSCRIPT
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
RI PT
24
RESs may not be able to meet the load demand completely. So a micro-grid with other fuel based sources of power generation is advisable. Micro-grid basically depend on local distributed energy sources. Cost of power generation in a micro-grid is higher than conventional thermal power generation, hence to economise the operation of micro-grid is of greater importance. Non-linear and discontinuous cost characteristics of fuel based sources opt for meta-heuristic as an appropriate method of optimisation. Complexity of generator scheduling problem increases with real time pricing (RTP). A mixed integer non-linear programming approach to single machine scheduling is presented in [3]. From the scheduling results, a better decision making possibility for the problem is observed. In this paper, anti-predatory particle swarm optimisation (APSO) method is used. In a micro-grid, cost from RESs are comparatively lower, making them as high priority energy sources. However, stochastic nature of renewable energy makes it another challenge under real time operation. Considering stochastic nature into benefit, an RTP strategy of micro-grid is presented in this paper. Swarm intelligence based APSO in optimisation of difficult problem is well experimented. Swarm intelligence and other stochastic methods are gaining popularity. A stochastic dynamic programming using greedy algorithm in a wholesale electricity market scenario is explained in [4]. Energy pricing for electric vehicle charging with RESs and energy storage is discussed. Further, an ant colony optimisation in comparison with PSO for energy management system is explained in [5]. Optimum scheduling of micro energy sources in a micro-grid is analysed, claiming improved performance and cost efficiency.
SC
23
M AN U
22
45
49 50 51 52 53 54 55
AC C
56
D
48
Rural electrification rate is much poor in many countries. World bank estimates, approximately 1.2 billion people do not have regular access to electricity. In Latin America, 33.8 million people live without electricity [6]. In Sub-Saharan Africa, around 600 million people live without using electricity [7]. In Bihar, India 75 million people live without electricity [8]. In Chile,79 locations are identified for rural electrification projects for which micro-grid is advised as feasible solution [6]. In Maharastra India, 329 villages are electrified in view of micro-grid concept [9]. An analysis for micro-grid feasibility in three regions of Senegal with population ranging from 250 to 1500 people in each village is given in [10]. The power demand for the same is identified in a range of 6KW to 45KW. Remote villages where to, the transmission grid extension is costlier, standalone micro-grid is desired. For large population, basic standard of living can be accomplished by micro-grid installation.
TE
47
EP
46
57 58 59
60
61
62
63 64 65 66 67
Effective and economic operation of micro-grid is vital for sustained development. Price-signal based control strategy is an elegant method for overall control of micro-grid. Penetration of RTP is difficult, a study on consumers with high demand response is identified to promote RTP [11]. Various policies on time-based-pricing and possible shifting and shaping of load profile is explained in [12]. Further, stochastic nature of RESs call for dynamic pricing a favourable option. A fast acting demand response is modelled and analysed with alternative models in [13]. Dynamic scheduling with the penetration of 2
ACCEPTED MANUSCRIPT
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
RI PT
73 74
SC
72
M AN U
71
D
70
wind energy with economy and RTP is discussed in [14]. Many possible cases of demand response scenario is explained. Further, a dynamic home energy management (HEM) system considering both load and source side is explained in [15]. An elegant explanation on simple ToU and RTP is given in [16] taking high WT power scenario. Similarly, evaluation of impact of RTP due to wind power generation is given in [17]. Awareness of ToU pricing considering 100 million water heaters installed in residential load is given in [18]. Again, usage based dynamic pricing for a smart grid is given in [19]. A practical approach to pricing based on closed loop approach from supply and demand is explained in [20]. Price based wind power utilisation in a deregulated electricity market is given in [21]. A price-responsive customers in a day-ahead scheduling scenario maximising profit of distribution network operator is discussed in [22]. Price based demand response strategy, in view of smart-grid implementation in a residential area in China is discussed in [23] and [24]. Incentive schemes can be given more importance, a virtual RTP considering consumers’ benefit is given in [25]. Linear and non-linear models on incentive based and price based demand response is explained in [26]. Price based demand response model, considering behaviour analysis of decision makers is given in [27]. Further, Solar powered micro-grid with 9,10 and 15 homes are simulated and a pricing mechanism is explained in [28]. Price based control can reduce consumption as well as emission, CO2 emission considering shifting of load to low price hour is given in [29]. Further, techno-economic evaluation of grid connected PV system, considering reduced carbon emission is analysed with the help of HOMER software in [30]. Model predictive control based RTP for commercial building is presented in [31]. The model claims to have easy control possibility for end-users. A survey on RTP for typical industrial customers in United States is discussed in [32]. Hence from the literature, many approach to incentive schemes with RTP and ToU using RESs pricing is observed. In this paper, a novel method of power-to-cost index based RTP is presented. Power-to-cost index is a measure of power generation from both renewable and non-renewable energy sources to that of cost involved in its generation. Obviously, the cost varies with magnitude of renewable power generation as well as, with non-linear cost characteristics of fuel based sources.
TE
69
EP
68
105
This paper is organised as follows: the economic emission dispatch (EED) problem formulation of micro-grid and methodology used is given in section 2. Various components of the micro-grid is presented in section 3. Section 4 gives discussion on simulation results with pricing strategy followed by conclusion in section 5.
106
2. Economic emission dispatch problem formulation
101
AC C
102 103 104
107 108 109 110
Minimising fuel cost of power generation in distributed generation (DG) meeting the equality and inequality constraints is called economic dispatch (ED). Emission dispatch or EED in a micro-grid is an emerging area of study. Since emission depends on non-linear characteristics of emission function from
3
ACCEPTED MANUSCRIPT
different sources, total minimum cost φ is given by φ = min
Ng X (FCM T , ECM T , FCF C )$/hr i=1
114 115 116
where, Ng is total number of generators FCM T is the fuel cost of MT ECM T is the emission cost of MT FCF C is the fuel cost of FC
SC
112 113
117
119 120 121 122
Load forecasting techniques are well explained in [33]. However, a typical power demand profile as shown in figure 1 is taken. Further, WT and PV generation profile taken is, as shown in figure 2. In the proposed standalone micro-grid, power generated by RESs are not stored and immediate dispatch gives the equality constraints as given below
M AN U
118
Pdh = Pgh + PP V h + PW T h
125 126 127
where, Pdh is the power demand in hth hour Pgh is the power generated by fuel sources in hth hour PP V h is the power generated by PV at hth hour PW T h is the power generated by WT at hth hour
128 129
The inequality constraints are the generation limits of FCs and MTs as given below
TE
130
min max PM Ti ≤ PM Ti ≤ PM Ti
132 133
where, th max min MT, rePM Ti and PM Ti are the minimum and maximum power limits of i spectively. max th PFmin FC, respecCi and PF Ci are the minimum and maximum power limits of i tively.
AC C
134 135
(3)
EP
max PFmin Ci ≤ PF Ci ≤ PF Ci
131
(2)
D
123 124
(1)
RI PT
111
136
137
138 139
140 141 142 143 144
Hence, minimising the objective function of equation 1 meeting the equality constraints in equation 2 and the inequality constraints in equation 3 gives desired EED problem in the micro-grid. 2.1. Methodology A standalone micro-grid is taken as an experimental study and the technology is identified to be an ideal solution for remote areas. It is assumed that, forecast values of power generated from WT and PV is readily available. A typical load curve is taken as ideal reference load curve. Further, transmission 4
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
1load-eps-converted-to.pdf
EP
TE
D
Figure 1: Load demand profile
AC C
2Windsolar-eps-converted-to.pdf
Figure 2: Wind-solar generation profile
5
ACCEPTED MANUSCRIPT
148 149 150 151 152
153 154 155 156 157 158 159 160 161 162 163 164
RI PT
147
losses within the micro-grid and reserve power generation is neglected. Hence, power generation cost is directly reflected as RTP of energy at the consumer end. PSO method of EED is compared with APSO for finding minimum total cost, this in turn is used to find average power-to-cost index value for whole day. Average power-to-cost index for the particular day is identified to be 6.69. The same is shown as dashed lines in figure 3. It is taken as reference for identifying incentive periods while the micro-grid is operating with integrated RESs in real time.
2.1.1. Particle swarm optimisation PSO is one of the popular and simplest method of optimisation for large non-linear problems. It was first introduced by Kennedy and Eberhart in the year 1995. In 2006, anti-predatory particle swarm optimisation was proposed by Immanuel and Thanushkodi [34]. PSO method is inspired from social survival behaviour of birds. Position and velocity of swarm of birds in three dimensional space is modelled into hyper-dimension, to obtain optimal solution for complex higher dimensional real world problems. Many such non-linear problems where solved using swarm intelligence based method. Parallel PSO based search method for solving large scale problem is efficiently applied by Subbaraj et. al. [35]. However, an upgrade to PSO is APSO method, which is used for solving the proposed problem.
SC
146
M AN U
145
165
170 171 172 173 174 175 176
AC C
177
D
169
TE
168
2.1.2. Power-to-cost index Power-to-cost index is the ratio of power generated to cost of power generation at any given hour. This value is obtained from minimum cost of ED without RESs. Hence, from the energy cost obtained for the given load profile, an average value of 6.69 is obtained as power-to-cost index without RESs. It is an index taken as a measure to interpret, the significant change in price of energy under the influence of stochastic RESs. Cost rate of 0.074$/kW for WT and 0.125$/kW for PV is taken from reports by US department of energy (DOE) [36]. Here we take the power generation profile from RESs pre-fixed, even though many approach to sizing of RESs using methods like PSO is available, as in [37]. Since the cost of generation is directly reflected onto customers, direct cost benefit due to RESs is modified into a new pricing strategy. A similar and innovative approach of hybrid energy sizing with a pre-fixed reliability index is also explained in [36].
EP
166 167
178 179 180
181
182 183 184 185 186 187 188
Since EED for DG is a non-linear problem, cost of generation varies with dynamic dispatch. Hence, the average of power-to-cost index for whole day is taken for further calculation. Figure 3 presents typical power-to-cost comparison, index crossing the line of averaged index is considered as incentive period. Whenever the index is greater than the averaged power-to-cost index, an incentive is provided. Incentives motivate customers to reduce energy price at that particular hour. The amount of incentive is a share of benefit due to RESs. Hence, an increase in renewable power generation due to its inherent nature, 6
RI PT
ACCEPTED MANUSCRIPT
SC
3power_to_cost-eps-converted-to.pdf
Figure 3: Power-to-cost index comparison
193 194 195 196 197 198 199 200 201
202 203 204 205
2.1.3. Anti-predatory PSO Anti-Predatory PSO is an upgrade to PSO method. APSO method is inspired from survival behaviour of birds avoiding worst case memories [34]. Flowchart for APSO method of dynamic EED problem with RESs and RTP is shown in figure 4. APSO methodology for energy pricing is given below t−1 t−1 Vijt = wt × Vijt−1 + c1 × r1 × Pbest − Xij ij
t−1 +c2 × r2 × Gt−1 besti − Xij
t−1 t−1 +c3 × r3 × Xij − Pworst i
t−1 +c4 × r4 × Xij − Gt−1 worsti
AC C
206
M AN U
192
D
191
is directly benefited as an increment in incentive. Similarly, cumulative reduction in power consumption also makes the new calculated index higher than averaged index, making the period falls under incentive period. This provision, drives the customers to reduce their consumption, indirectly minimising the excess power demand. Hence, the proposed method is identified to have the potential to accommodate stochastic RESs as well as to put a control over power consumption.To reduce carbon emission is also an important concern of nations. A review on reduced carbon emission with government subsidy is analysed for European countries in [38].It is identified that renewable energy sources from developed countries are competent enough with the proposed scheme. Similar method of impact of renewable energy source installation in residential areas and net metering into sufficient incentive in comparison with a levelized cost of electricity is explained in [39].
TE
190
EP
189
207 208 209 210 211
where, t is the iteration count wt is inertia coefficient at t t−1 Xij is position vector at t-1 t Vij is velocity vector at t 7
(4)
ACCEPTED MANUSCRIPT
Method
Minimum Cost
213 214 215 216 217
218.865 208.542 204.557 192.109
220 221
7.618 6.909 5.525 6.409
Variance
Global hits
58.041 47.736 30.531 41.077
97 95 99 98
where, t Xij is position vector at t t−1 Xij is position vector at t-1 t Vij is velocity vector at t
(5)
D
218
Standard Deviation
Vijt−1 is velocity vector at t-1 c1 , c3 are self best and self worst confidence factor c2 , c4 are swarm best and swarm worst confidence factor t−1 Pbest , Gt−1 bestij are personal and global best at t-1 ij t−1 Pworstij , Gt−1 worstij are personal and global worst at t-1 r1 , r2 , r3 , r4 are random number between 0 and 1 t−1 t Xij = Xij + Vijt
219
241.991 226.24 219.728 214.526
M AN U
212
202.980 192.386 189.874 178.445
Maximum Cost
SC
PSO without RESs PSO with RESs APSO without RESs APSO with RESs
Average Cost
RI PT
Table 1: PSO and APSO Statistics for 100 Trials
TE
222
Statistics for PSO and APSO methods with 100 trials is given in table 1. Confidence parameters c1 , c2 , c3 and c4 are obtained from table 2 and table 3.
225
3. Micro-grid
226 227
Micro-grid can be a complete energy solution for remote areas where power grid extension is costlier. Micro-grid consists of DG including RESs. There are various energy management system (EMS) schemes proposed for such technology. An elegant demonstration of real time EMS for a micro-grid using genetic algorithm is given in [40]. Smart EMS (SEMS) for micro-grid economic operation considering day ahead prediction of PV power with weather condition is proposed in [41]. SEMS having forecasting module, energy storage system management module and optimisation module is implemented. SEMS combined the complex pricing and economic dispatch to a single objective optimisation problem, a matrix real-coded genetic algorithm is used for optimisation. Major challenge in micro-grid operation is stochastic power generation from RESs and coordination between them. Planning a micro-grid with optimal mix of distributed energy sources is given in [42]. Robust optimisation method is adopted considering forecast errors in load, stochastic renewable generation and
AC C
228
EP
224
223
229 230 231 232 233 234 235 236 237 238 239
8
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE
D
4flowchart-eps-converted-to.pdf
Figure 4: Dynamic EED flowchart with energy pricing using APSO
9
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
5uG-eps-converted-to.pdf
Figure 5: Proposed micro-grid layout
243 244 245 246 247 248 249 250 251 252
Block diagram layout for proposed micro-grid is shown in figure 5. Power flow from MT, FC, WT and PV is illustrated along with wide area network (WAN) signals. EMS retrieve the status of fuel based sources and load demand profile. Forecast signals can be used to predict day-ahead power generation profiles of WT and PV. Followed up cost analysis of power generation decides the pricing, this signal is send to customers using WAN in real time.
AC C
253
D
242
market prices. Similarly, robust energy management with worst-case scenario optimisation, considering exchange of electricity price is given in [43]. Problem formulation, minimising operational cost and power loss is explained in [44]. Intermittent nature of RESs along with optimal charging of plug-in electric vehicles, distributed generators and distributed energy storage devices is considered. Managing DGs, finally benefit the utility, customers or both and, a simple energy pricing strategy always gives positive response from customers.
TE
241
EP
240
254
255
256 257
258 259 260
3.1. Micro-turbines MTs with maximum power generation of 30KW is considered as a DG type. Capstone manufacture such MT with natural gas as its fuel [45]. Fuel cost and emission cost of the MT is given as follows FCM T = a × p2M Ti + b × PM Ti + c
where, FCM T is the total fuel cost of MTs a, b, c are the MT cost coefficients
10
(6)
ACCEPTED MANUSCRIPT
261
PM Ti is the power generated by ith MT
262
263 264 265 266 267
where, ECM T is the total emission cost of MTs ECO2i is the carbon dioxide emission coefficient of ith MT ESO2i is the sulphur dioxide emission coefficient of ith MT EN Oxi is the nitrogen oxides emission coefficient of ith MT
270 271 272 273
3.2. Fuel cells FCs with maximum power rating of 30KW is taken for the micro-grid. FC convert hydrogen fuel into electrical energy. They are pollution free however, initial cost of installation is high [45]. Cost of power generation from FC is given by
M AN U
269
SC
268
FCF C = KF Ci × PF Ci 274 275 276 277
(7)
RI PT
ECM T = ECO2i × PM Ti + ESO2i × PM Ti + EN Oxi × PM Ti
(8)
where, FCF C is the total fuel cost of FCs KF Ci is the fuel cost coefficient of ith FC PF Ci is the power generated by ith FC
281 282 283 284
t0
AC C
285
3.3. Renewable energy sources 3.3.1. Photo voltaic Power generated by PV mode depends on the level of irradiation, system efficiency and temperature of geographical area. More research on PV life cycle and economic analysis in the event of uncertainty is given in [46]. Hence power from PV is stochastic in nature. Power generation formulation for PV is given by [47] Z t PP V = AP V × ηP V × I(t) × f (t) × d(t) (9)
TE
280
EP
279
D
278
286 287
288
289
290 291
292
where, PP V is the power generated by PV in MW AP V is the total area of PV panel ηP V is the PV system efficiency I(t) is the hourly irradiation in KW hr/m2 f (t) is the radiance density t0 and t are initial and final time of operation respectively
293
11
RI PT
ACCEPTED MANUSCRIPT
SC
6parameter-eps-converted-to.pdf
Figure 6: APSO confidence parameter interpolation for 100 trials
295 296 297 298
3.3.2. Wind turbine WT is the largest contributors of RES. Power generation from WT depends on geographical area, wind velocity and capacity of WT. More study on various types and characteristics of WT is given in [48]. Power generated from WT is given by [47]
M AN U
294
PW T = 1/2 × ρa × AW T × Cp,W T Z t ×ηW T × va3 × f (t) × d(t)
(10)
302 303 304 305 306
AC C
307
TE
301
where, PW T is the power generated by WT AW T is the WT swept area ρa is the air density, 1.225Kg/m3 Cp,W T is the coefficient of WT performance ηW T is the combined efficiency of WT and generator va is the wind velocity in m/sec f (t) is the wind probability density function
EP
299 300
D
t0
308
309
310 311 312 313 314 315 316
4. Simulation results and pricing strategy Modelling of the proposed micro-grid is explained with a layout, appropriate assumptions and methodology. Stochastic method of APSO is used to minimise the EED problem. With the help of flowchart, EED problem into RTP is deduced. Confidence parameter selection graph with RESs and without RESs is given in figure 6. It is identified that cost decreases to particular parameter and then increases. Hence inferred that, parameter defines the convergence to optimal minimum value in stochastic optimisation. APSO statistics of minimum cost from 1000 iteration for 100 trials without and with RESs is presented in
12
ACCEPTED MANUSCRIPT
c4
Minimum Cost
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
225.107 223.656 221.693 214.311 202.074 198.795 195.461 193.607 192.723 191.324 190.758 192.366 189.874 193.724 193.751
Average Cost 251.05 244.59 238.855 231.546 219.236 210.82 208.052 205.276 203.676 202.831 204.205 205.77 204.557 207.293 210.248
Maximum Cost 271.142 267.563 257.301 252.977 238.09 226.515 219.423 221.496 222.508 217.026 216.446 222.528 219.728 222.872 231.904
SD
Variance
Global hits
9.065 7.767 7.908 7.5 6.993 5.776 5.926 5.654 6.287 5.784 5.629 5.849 5.525 6.289 7.316
82.173 60.328 62.537 56.243 48.901 33.363 35.113 31.97 39.521 33.451 31.684 34.211 30.531 39.553 53.529
81 96 98 98 94 100 100 100 99 100 100 99 99 99 93
SD
Variance
Global hits
8.784 8.839 7.078 7.847 7.373 6.248 6.523 6.555 5.165 5.582 6.409 6.402 6.262 6.442 7
77.167 78.122 50.095 61.582 54.354 39.032 37.219 42.963 26.68 31.153 41.077 40.983 39.219 41.495 48.993
73 74 95 93 98 100 96 95 99 98 97 97 96 89 91
SC
c3
M AN U
c2
D
c1
RI PT
Table 2: APSO Parameter Selection for 100 Trials without RESs
c3
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
AC C
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
c2
c4
EP
c1
TE
Table 3: APSO Parameter Selection for 100 Trials with RESs
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
Minimum Cost
211.66 207.897 208.203 204.137 194.524 187.002 180.739 178.996 182.172 179.528 178.445 180.763 179.245 180.295 182.803
Average Cost 236.786 231.324 227.368 219.101 207.876 199.1 196.012 192.875 192.159 190.711 192.109 193.02 193.723 197.556 199.945
13
Maximum Cost 253.2 257.389 244.754 236.032 231.252 213.709 217.868 210.732 210.442 207.761 214.526 215.518 215.596 212.294 219.351
ACCEPTED MANUSCRIPT
318 319 320 321
table 2 and table 3 respectively. Global hits are identified from 15% of minimum cost from 100 trials. For the same minimum cost, dynamic EED schedule without and with RESs is presented in table 4 and table 5 respectively. Overall power generation meeting load demand with RESs using APSO is graphically represented in figure 7.
RI PT
317
322
328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350
Uncertainty scenario analysis for various time period on the same day is carried out. For a 10% increase in load demand at 3AM, 5AM, 8AM, 5PM and 7PM, the RTP is shown in figure 9. Similarly, for a 10% decrease in load demand is shown in figure 10. From both the case studies, it is identified that, new power-to-cost index varies as expected to that of set value of 6.69. That is, for increased load demand at 5PM, the index decrease to 8.98 to the initial case of 9.69 and, for decreased load demand, the index increases to 13.94 to that of 9.69. Note that falling of new power-to-cost index below the set value might have nullified the incentive at that particular hour. However, the renewable power generation is so high, so as to keep the period within incentive period. From tabulation 7 and 8 , the RTP of 2.919$ to that of 4.665$ in the initial case and 4.786$ with RESs is observed. Hence, a price-signal send to reduce energy consumption to 10% will dramatically reduce the fuel cost and followed emission cost by proposed RTP at the utility side. Similar techniques of sending simple message for reduced consumption and load shifting is given in [16].
AC C
351
SC
327
M AN U
326
D
325
With power-to-cost index, an awareness is brought towards reduction in pricing by virtue of low cost power from RESs. More explanation for the proposed power-to-cost index is given in section 2. Once customers become aware of power-to-cost index, it can be used as a bias towards reduced energy consumption. Signal from smart-meter can also be shared to personal devices (cellphone) for increased awareness and followed up decision. Customers will be able to monitor anticipated energy price for next hour. That is by broadcasting a choice to bring down price within the incentive period by virtue of reduced energy consumption. A smart plug compatible with such an operation is discussed in [49]. Dynamic coordination among home appliances to turn them ON or OFF under various demand response scenario is presented in [50]. An optimal scheduling using home energy management technique is obtained. Overall price profile comparison is given in table 6, the same is shown in figure 8. Note that for many instants, pricing without RESs are higher than pricing with RESs. Pricing with RESs will not make significant bias towards reduction in consumption of energy. Similarly, for some instants price control with RESs is higher than pricing with RESs however, it is never greater than pricing without RESs. Hence, price control with RESs is attractive as it adds a control feature apart from RTP for the same daily total price. Price control strategy is achieved with incentives and keeping power-to-cost index as benchmark. Assuming immediate usage of power from RESs, resultant reduction in power from fuel based sources reduce cost of generation.
TE
324
EP
323
352 353 354 355 356 357 358 359 360 361
14
Table 4: Dispatch Schedule without RESs
Load, W
M T1
M T2
M T3
M T4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
32080 26803 24109 22313 18047 18047 26803 35897 48471 47460 46787 54196 54645 49481 48246 45103 45215 62953 80690 87875 88998 80578 68005 49481
355 185 37 53 143 521 319 447 131 19 2 573 95 1177 711 88 1011 777 53 710 912 355 34 1145
355 185 37 53 143 521 319 4777 5124 19 2 5612 3588 1172 5676 5435 11189 688 11611 13703 912 355 3228 1145
355 185 37 53 143 521 1084 1160 3357 7046 2 574 3487 4993 5245 1187 88 777 1930 5477 11971 6772 7192 2716
5179 185 37 53 143 521 271 447 131 2563 2 4912 5999 10300 3754 88 88 23755 15033 10156 19064 19525 18946 1231
F C1
F C2
Cost, $/hr
3355 3133 3052 3090 3131 3500 3270 3389 10721 8142 24443 12638 12125 4091 7086 8341 3096 7056 22858 27910 26204 24555 9121 13873
22481 22930 20909 19011 14344 12463 21540 25677 29007 29671 22336 29887 29351 27748 25774 29964 29743 29900 29205 29919 29935 29016 29484 29371
4.078 2.933 2.659 2.633 2.626 3.164 3.296 4.836 7.176 6.592 7.863 8.208 8.166 7.667 7.942 6.086 7.088 11.268 15.344 17.492 17.904 15.306 12.073 7.473
D
M AN U
SC
Hour
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE
Total
7gen_load-eps-converted-to.pdf
Figure 7: Overall generation meeting load demand with RESs using APSO
15
189.874
ACCEPTED MANUSCRIPT
RI PT
Table 5: Dispatch Schedule with RESs
M T1
M T2
M T3
M T4
F C1
F C2
WT
PV
Cost, $/hr
32080 26803 24109 22313 18047 18047 26803 35897 48471 47460 46787 54196 54645 49481 48246 45103 45215 62953 80690 87875 88998 80578 68005 49481
236 196 402 619 557 310 587 154 44 204 930 50 77 134 84 974 181 106 54 17 5 473 906 32
282 196 402 619 557 310 587 154 44 2828 930 50 77 134 84 2269 181 2355 6768 11928 1934 473 906 2441
300 196 402 619 557 310 587 154 3807 204 930 50 6552 243 84 5453 181 3051 12904 3095 6563 8917 10353 32
236 7022 402 619 557 25 587 154 9524 2893 6858 15985 6475 9376 4786 974 3549 17251 7132 9447 20646 23015 10580 10140
3279 3156 3378 3622 3514 3295 3491 3133 3078 7191 14392 3059 5868 3156 13921 3889 4087 6329 17553 29608 28550 18587 14825 7439
27663 15339 18973 16083 9801 11266 17733 24139 27436 28757 15938 28150 29763 28858 23441 25764 29264 28938 29214 29608 28813 27185 29457 29115
84 698 150 132 2504 2531 3231 8000 3874 3420 3575 2426 833 2585 1241 1797 4575 2738 6086 3970 2487 1928 978 282
0 0 0 0 0 0 0 9 664 1963 3234 4426 5000 4995 4605 3983 3197 2185 979 202 0 0 0 0
3.21 4.083 3.177 3.441 3.322 2.939 3.643 3.518 6.182 5.784 8.285 7.375 7.404 5.758 6.949 6.528 4.665 9.81 14.437 16.182 16.983 16.059 12.004 6.707
D
M AN U
SC
Load, W
178.445
EP
TE
Total
Table 6: Pricing strategy comparison tabulation
1
2
3
4
5
6
7
8
9
10
11
12
Price without RESs Price with RESs Price control with RESs
4.078 3.21 3.505
2.933 4.083 2.933
2.659 3.177 2.285
2.633 3.441 2.633
2.626 3.322 2.626
3.164 2.939 3.164
3.296 3.643 2.833
4.836 3.518 4.156
7.176 6.182 6.167
6.592 5.784 5.665
7.863 8.285 7.863
8.208 7.375 7.053
Hour
13
14
15
16
17
18
19
20
21
22
23
24
Price without RESs Price with RESs Price control with RESs
8.166 7.404 7.018
7.667 5.758 6.589
7.942 6.949 6.825
6.086 6.528 5.23
7.088 4.665 6.091
11.268 9.81 11.268
15.344 14.437 15.344
17.492 16.182 17.492
17.904 16.983 17.904
15.306 16.059 15.306
12.073 12.004 12.073
7.473 6.707 6.422
AC C
Hour
16
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
8price-eps-converted-to.pdf
Figure 8: Price profile comparison diagram
Table 7: Dispatch Schedule with and without RESs under Uncertainty of 10 % Increment in Load
Load, W
M T1
M T2
3 3 5 5 8 8 17 17 19 19
26520 26520 19851 19851 39486 39486 49737 49737 88760 88760
3 2 3 15 214 6 113 250 145 920
3 2 3 15 214 6 938 250 4387 4224
M T3
M T4
F C1
F C2
WT
PV
Cost, $/hr
3 2 3 15 214 6 113 250 6161 4910
3 2 3 15 5785 6 17683 8098 18323 12991
3002 3002 3004 3014 3193 3008 3692 3224 29751 29325
23506 23360 16835 14274 29867 28446 27191 29892 29993 29325
0 150 0 2504 0 8000 0 4575 0 6086
0 0 0 0 0 9 0 3197 0 979
2.678 2.683 2.483 2.610 4.216 3.418 7.609 4.786 16.980 15.831
AC C
EP
TE
D
Hour
9price2-eps-converted-to.pdf
Figure 9: RTP with 10% load increment at various hours
17
RI PT
ACCEPTED MANUSCRIPT
Table 8: Dispatch Schedule with and without RESs under Uncertainty of 10 % Decrement in Load
M T1
M T2
M T3
M T4
3 3 5 5 8 8 17 17 19 19
21698 21698 16242 16242 32307 32307 40694 40694 72621 72621
5 2 3 2 10 7 135 41 36 412
5 2 3 2 10 7 135 41 2653 412
5 2 3 2 10 7 135 41 3072 412
5 2 3 2 10 7 3891 42 12067 10204
F C1
F C2
SC
Load, W
3003 3005 3002 3004 3009 3006 6425 3037 25216 24780
18675 18535 13228 10726 29254 21264 29972 29719 29577 29336
EP
TE
D
M AN U
Hour
AC C
10price3-eps-converted-to.pdf
Figure 10: RTP with 10% load decrement at various hours
18
WT
PV
Cost, $/hr
0 150 0 2504 0 8000 0 4575 0 6086
0 0 0 0 0 9 0 3197 0 979
2.539 2.542 2.376 2.487 2.857 3.208 4.529 2.919 12.425 11.300
ACCEPTED MANUSCRIPT
364 365 366 367 368 369 370 371 372 373
In this paper, dynamic EED in a typical micro-grid is presented. EMS module is assigned to coordinate generation as well as pricing strategy. It is identified that availability of power generation profile from stochastic WT and PV contributed in scheming dynamic EED. It is also inferred that APSO outperform PSO method in economising dynamic EED by 7.24% with RESs. Finally, new pricing strategy with power-to-cost index is presented focusing on stochastic nature of RESs. In a realistic scenario, smart-meters compatible of receiving WAN (WiMAX) signal is considered to be a feasible option for monitoring the RTP. Further, the method with behavioural response is identified to lower the excess power demand, hence resulting to peak demand reduction, lower generation cost, reduced emission and economy within the micro-grid.
RI PT
363
5. Conclusion
SC
362
378
379 380 381 382 383 384
385 386 387
388 389
[2] S. Chandel, R. Shrivastva, V. Sharma, P. Ramasamy, Overview of the initiatives in renewable energy sector under the national action plan on climate change in India, Renewable and Sustainable Energy Reviews 54 (2016) 866–873. doi:10.1016/j.rser.2015.10.057. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032115011363 [3] S. Lee, B. Do Chung, H. W. Jeon, J. Chang, A dynamic control approach for energy-efficient production scheduling on a single machine under timevarying electricity pricing 165 (2017) 552–563. [4] C. Luo, Y.-F. Huang, V. Gupta, Stochastic dynamic pricing for EV charging stations with renewables integration and energy storage 9 (2017) 1494 – 1505.
AC C
390
[1] Executive Summary of Power Sector - March 2016, central electricity authority (cea) (2016). URL http://www.cea.nic.in/monthlyexesummary.html
D
377
TE
376
6. References
EP
375
M AN U
374
391
392 393
394 395
396 397
398 399 400
[5] M. Marzband, E. Yousefnejad, A. Sumper, J. L. DomnguezGarca, Real time experimental implementation of optimum energy management system in standalone microgrid by using multilayer ant colony optimization 75 (2016) 265 – 274. doi:https: //doi.org/10.1016/j.ijepes.2015.09.010. URL http://www.sciencedirect.com/science/article/pii/ S0142061515003920
[6] It Takes a Village: Social SCADA and Approaches to Community Engagement in Isolated Microgrids, IEEE Power and Energy Magazine 12 (4) (2014) 60–69. doi:10.1109/MPE.2014.2317419.
19
ACCEPTED MANUSCRIPT
409 410 411 412 413
414 415 416 417 418 419
420 421 422 423 424 425
426 427 428
429 430
[8] L. Molyneaux, L. Wagner, J. Foster, Rural electrification in India: Galilee Basin coal versus decentralised renewable energy micro grids, Renewable Energy 89 (2016) 422–436. doi:10.1016/j.renene.2015.12.002. URL http://linkinghub.elsevier.com/retrieve/pii/ S0960148115305061
[9] R. Bhoyar, S. Bharatkar, Potential of MicroSources, Renewable Energy sources and Application of Microgrids in Rural areas of Maharashtra State India, Energy Procedia 14 (2012) 2012–2018. doi:10.1016/j.egypro.2011.12.1202. URL http://linkinghub.elsevier.com/retrieve/pii/ S1876610211046224 [10] H. Camblong, J. Sarr, A. Niang, O. Curea, J. Alzola, E. Sylla, M. Santos, Micro-grids project, Part 1: Analysis of rural electrification with high content of renewable energy sources in Senegal, Renewable Energy 34 (10) (2009) 2141–2150. doi:10.1016/j.renene.2009.01.015. URL http://linkinghub.elsevier.com/retrieve/pii/ S0960148109000548 [11] G. Wang, Q. Zhang, H. Li, Y. Li, S. Chen, The impact of social network on the adoption of real-time electricity pricing mechanism 142 (2017) 3154– 3159. [12] C. Eid, E. Koliou, M. Valles, J. Reneses, R. Hakvoort, Time-based pricing and electricity demand response: Existing barriers and next steps 40 (2016) 15 – 25. doi:https://doi.org/10.1016/j.jup.2016.04.001. URL http://www.sciencedirect.com/science/article/pii/ S0957178716300947
AC C
431
RI PT
408
SC
407
M AN U
406
D
405
[7] K. Lee, E. Brewer, C. Christiano, F. Meyo, E. Miguel, M. Podolsky, J. Rosa, C. Wolfram, Electrification for Under Grid households in Rural Kenya, Development Engineering 1 (2016) 26–35. doi:10.1016/j.deveng.2015.12.001. URL http://linkinghub.elsevier.com/retrieve/pii/ S235272851530035X
TE
403 404
URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6832818
EP
401 402
432 433
434
435 436
437 438
439 440 441
[13] D. P. Chassin, D. Rondeau, Aggregate modeling of fast-acting demand response and control under real-time pricing 181 (Supplement C) (2016) 288 – 298. doi:https://doi.org/10.1016/j.apenergy.2016.08.071. URL http://www.sciencedirect.com/science/article/pii/ S0306261916311588 [14] Y. Zhai, K. Biel, F. Zhao, J. W. Sutherland, Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing 66 (1) (2017) 41–44. 20
ACCEPTED MANUSCRIPT
449
450 451 452 453 454
455 456 457 458 459 460
461 462 463 464 465
466 467
468 469 470 471
[17] R. Sioshansi, W. Short, Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation, IEEE Transactions on Power Systems 24 (2) (2009) 516–524. doi:10.1109/TPWRS.2008.2012184. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=4783090
[18] J. J. Shah, M. C. Nielsen, T. S. Shaffer, R. L. Fittro, Cost-Optimal Consumption-Aware Electric Water Heating Via Thermal Storage Under Time-of-Use Pricing, IEEE Transactions on Smart Grid (2015) 1–8doi:10.1109/TSG.2015.2483502. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=7353201 [19] X. Liang, X. Li, R. Lu, X. Lin, X. Shen, UDP: Usage-Based Dynamic Pricing With Privacy Preservation for Smart Grid, IEEE Transactions on Smart Grid 4 (1) (2013) 141–150. doi:10.1109/TSG.2012.2228240. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6471882 [20] J. He, C. Zhao, L. Cai, P. Cheng, L. Shi, Practical closed-loop dynamic pricing in smart grid for supply and demand balancing 89 (2018) 92–102. [21] Q. Wang, J. Wang, Y. Guan, Price-Based Unit Commitment With Wind Power Utilization Constraints, IEEE Transactions on Power Systems 28 (3) (2013) 2718–2726. doi:10.1109/TPWRS.2012.2231968. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6401219
AC C
472
RI PT
448
SC
447
[16] P. Grnewald, E. McKenna, M. Thomson, Keep it simple: time-of-use tariffs in high-wind scenarios, IET Renewable Power Generation 9 (2) (2015) 176–183. doi:10.1049/iet-rpg.2014.0031. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2014.0031
M AN U
445 446
D
444
[15] O. Elma, A. Tackaraolu, A. T. nce, U. S. Selamoullar, Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts 134 (2017) 206–220.
TE
443
EP
442
473
474
475 476 477
478 479
480 481
[22] M. Mazidi, H. Monsef, P. Siano, Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks 115 (2016) 103 – 116. doi:https://doi.org/10.1016/j.enconman.2016.02.030. URL http://www.sciencedirect.com/science/article/pii/ S0196890416300541
[23] C. Yang, C. Meng, K. Zhou, Residential electricity pricing in china: The context of price-based demand response 81 (2017) 2870–2878. [24] H. Wang, H. Fang, X. Yu, S. Liang, How real time pricing modifies chinese households electricity consumption 178 (2017) 776–790. 21
ACCEPTED MANUSCRIPT
489 490 491 492 493 494
495 496 497 498 499
500 501 502 503 504
505 506 507 508 509 510
511
[27] S. Sekizaki, I. Nishizaki, T. Hayashida, Electricity retail market model with flexible price settings and elastic price-based demand responses by consumers in distribution network 81 (2016) 371 – 386. doi:https://doi.org/10.1016/j.ijepes.2016.02.029. URL http://www.sciencedirect.com/science/article/pii/ S0142061516302915
[28] S. Krishnamurthy, R. Tzoneva, Multi Objective Dispatch Problem with Valve Point Effect Loading of Fuel Cost and Emission Criterion, International Journal of Computer and Electrical Engineering 4 (5) (2012) 775. URL http://search.proquest.com/openview/ d114e7a7567a891236a384da7dbcfd87/1?pq-origsite=gscholar [29] T. Hahn, Z. Tan, W. Ko, Design of Time-Varying Rate Considering CO2 Emission, IEEE Transactions on Smart Grid 4 (1) (2013) 383–389. doi:10.1109/TSG.2012.2234771. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6449340 [30] V. Tomar, G. Tiwari, Techno-economic evaluation of grid connected PV system for households with feed in tariff and time of day tariff regulation in new delhi a sustainable approach 70 (2017) 822–835. doi:10.1016/j.rser.2016.11.263. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032116310498 [31] N. T. Mbungu, R. M. Naidoo, R. C. Bansal, Real-time electricity pricing: TOU-MPC based energy management for commercial buildings 105 (2017) 3419–3424.
AC C
512
RI PT
488
SC
487
M AN U
486
[26] M. Rahmani-andebili, Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets 132 (2016) 115 – 124. doi:https://doi.org/10.1016/j.epsr.2015.11.006. URL http://www.sciencedirect.com/science/article/pii/ S0378779615003326
D
484 485
[25] J. Ji, M. Sun, B. C. Ampimah, Design of virtual real-time pricing model based on power credit 142 (2017) 2669–2676.
TE
483
EP
482
513
514
515
516
517 518 519
520 521
[32] N. Nezamoddini, Y. Wang, Real-time electricity pricing for industrial customers: Survey and case studies in the united states 195 (2017) 1023–1037. [33] A. R. Khan, A. Mahmood, A. Safdar, Z. A. Khan, N. A. Khan, Load forecasting, dynamic pricing and dsm in smart grid: A review, Renewable and Sustainable Energy Reviews 54 (Supplement C) (2016) 1311 – 1322. doi:https://doi.org/10.1016/j.rser.2015.10.117. URL http://www.sciencedirect.com/science/article/pii/ S136403211501196X
22
ACCEPTED MANUSCRIPT
528 529 530 531 532 533
534 535 536 537
538 539 540 541 542
543 544 545 546 547
548 549 550 551
[35] P. Subbaraj, R. Rengaraj, S. Salivahanan, T. Senthilkumar, Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem 32 (9) (2010) 1014–1023. doi:10.1016/j.ijepes.2010.02.003. URL http://linkinghub.elsevier.com/retrieve/pii/ S0142061510000566 [36] Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2015 (2015). URL https://www.eia.gov/forecasts/archive/aeo15/pdf/ electricity_generation_2015.pdf [37] M. A. Mohamed, A. M. Eltamaly, A. I. Alolah, Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems 77 (2017) 515–524. doi:10.1016/j.rser.2017.04.048. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032117305506 [38] B. Bakhtyar, A. Fudholi, K. Hassan, M. Azam, C. Lim, N. Chan, K. Sopian, Review of CO2 price in europe using feed-in tariff rates 69 (2017) 685–691. doi:10.1016/j.rser.2016.11.146. URL http://linkinghub.elsevier.com/retrieve/pii/ S136403211630911X [39] S. Comello, S. Reichelstein, Cost competitiveness of residential solar PV: The impact of net metering restrictions 75 (2017) 46–57. doi:10.1016/j.rser.2016.10.050. URL http://linkinghub.elsevier.com/retrieve/pii/ S136403211630702X
AC C
552
RI PT
527
SC
526
M AN U
525
D
524
[34] A. I. Selvakumar, K. Thanushkodi, Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems, Electric Power Systems Research 78 (1) (2008) 2–10. doi:10.1016/j.epsr.2006.12.001. URL http://linkinghub.elsevier.com/retrieve/pii/ S0378779606002914
TE
523
EP
522
553
554 555
556 557
558
559 560 561 562
[40] M. Elsied, A. Oukaour, T. Youssef, H. Gualous, O. Mohammed, An advanced real time energy management system for microgrids 114 (2016) 742 – 752. doi:https://doi.org/10.1016/j.energy.2016.08.048. URL http://www.sciencedirect.com/science/article/pii/ S0360544216311616 [41] C. Chen, S. Duan, T. Cai, B. Liu, G. Hu, Smart energy management system for optimal microgrid economic operation, IET Renewable Power Generation 5 (3) (2011) 258. doi:10.1049/iet-rpg.2010.0052. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2010.0052 23
ACCEPTED MANUSCRIPT
571 572
573 574 575 576 577
578 579 580 581 582 583
584 585 586 587 588 589
590 591 592 593
[44] W. Su, J. Wang, J. Roh, Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources, IEEE Transactions on Smart Grid 5 (4) (2014) 1876–1883. doi:10.1109/TSG.2013.2280645. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6662462 [45] R. Lasseter, A. Akhil, C. Marnay, J. Stephens, J. Dagle, R. Guttromson, A. Meliopoulous, R. Yinger, J. Eto, The CERTS microgrid concept, White paper for Transmission Reliability Program, Office of Power Technologies, US Department of Energy 2 (3) (2002) 30. URL http://www.westernsunsystems.comorwww.gosolarcalifornia. org/research/notices/2002-05-02_WORKSHOP_SUPP.PDF [46] S. A. Kalogirou, Chapter 12 - solar economic analysis, in: S. A. Kalogirou (Ed.), Solar Energy Engineering (Second Edition), second edition Edition, Academic Press, Boston, 2014, pp. 701 – 734. doi:https://doi.org/10.1016/B978-0-12-397270-5.00012-1. URL http://www.sciencedirect.com/science/article/pii/ B9780123972705000121 [47] K. Kusakana, Optimal scheduled power flow for distributed photovoltaic/wind/diesel generators with battery storage system 9 (8) (2015) 916–924. doi:10.1049/iet-rpg.2015.0027. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2015.0027
AC C
594
RI PT
570
[43] Y. Xiang, J. Liu, Y. Liu, Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load, IEEE Transactions on Smart Grid (2015) 1–1doi:10.1109/TSG.2014.2385801. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=7010943
SC
568 569
M AN U
567
D
565 566
[42] A. Khodaei, S. Bahramirad, M. Shahidehpour, Microgrid planning under uncertainty, IEEE Transactions on Power Systems 30 (5) (2015) 2417– 2425. URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber= 6920097
TE
564
EP
563
595
596 597 598
599 600
601 602 603
[48] S. A. Kalogirou, Chapter 13 - wind energy systems, in: S. A. Kalogirou (Ed.), Solar Energy Engineering (Second Edition), second edition Edition, Academic Press, Boston, 2014, pp. 735 – 762. doi:https://doi.org/10.1016/B978-0-12-397270-5.00013-3. URL http://www.sciencedirect.com/science/article/pii/ B9780123972705000133 [49] V. P. Ramavarapu, R. Sowers, R. S. Sreenivas, A smart power outlet for electric devices that can benefit from real-time pricing (2017) 11–17doi: 10.1109/ICCEREC.2017.8226673. 24
ACCEPTED MANUSCRIPT
605 606
AC C
EP
TE
D
M AN U
SC
607
[50] A. Khalid, N. Javaid, M. Guizani, M. Alhussein, K. Aurangzeb, M. Ilahi, Towards dynamic coordination among home appliances using multiobjective energy optimization for demand side management in smart buildings 99 (2018) 1–1.
RI PT
604
25
TE
D
M AN U
TE
D
M AN U
TE
D
M AN U
CE
ED
PT M AN US
C
ED
PT
M AN US
TE
D
M AN U
TE
D
M AN U
ED
PT
M AN US
TE
D
M AN
TE
D
M AN
ACCEPTED MANUSCRIPT Research Highlights:
Power-to-cost index is proposed as a real time pricing strategy.
•
Remote/ standalone micro-grid is taken as a case study.
•
Wind and solar energy into pricing is considered.
•
Economic emission dispatch with numerical results for single day is presented.
•
Claims better control to lower peak demand, generation cost and emission cost.
AC C
EP
TE D
M AN U
SC
RI PT
•
S0960-1481(18)30535-4
DOI:
10.1016/j.renene.2018.05.016
Reference:
RENE 10068
To appear in:
Renewable Energy
Received Date: 9 September 2016 Revised Date:
1 April 2018
Accepted Date: 3 May 2018
Please cite this article as: Anand H, Ramasubbu R, A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources, Renewable Energy (2018), doi: 10.1016/j.renene.2018.05.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
4
Hithu Ananda , Rengaraj Ramasubbub
6 7 8
9
a Department
of Electrical and Electronics Engineering, Agni College of TechnologyChennai 600 130, Tamil Nadu, India. b Department of Electrical and Electronics Engineering, SSN College of EngineeringChennai 603 110, Tamil Nadu, India.
SC
5
Abstract
M AN U
2
RI PT
3
A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources
1
10
Keywords: Renewable energy sources, micro-grid, real time pricing, dynamic economic emission dispatch, anti-predatory particle swarm optimisation
AC C
11
EP
TE
D
Micro-grid is a complete energy solution for remote areas facing energy crisis however, energy cost from fuel based sources in a micro-grid is higher than conventional sources. So price-signal based control strategy for the benefit of utility and customers by virtue of renewable integration is analysed. Renewable energy has become less costiler than fuel based sources in a micro-grid. Stochastic power generated from wind turbine (WT) and photo voltaic (PV) along with economic generation from micro-turbine (MT) and fuel cell (FC) is optimised. Anti-predatory particle swarm optimisation (APSO) for non-linear dynamic economic emission dispatch of micro-grid for 24 hours with and without renewable energy sources (RESs) is analysed. APSO method with RESs is identified to give better fuel-cost-minimum. Power-to-cost index is proposed as a simple real time pricing (RTP) strategy. The strategy is identified to have the potential to bring down power demand and peak reduction followed by additional reduction in emission and fuel cost in any micro-grid.
12
13
14 15 16 17 18 19 20 21
1. Introduction Technology is advancing at a higher rate, weather forecast is much accurate than before. Years of acquired wind and solar data has brought prediction closer to real time events. Hence the advancement in technology ensure that, installation of renewable energy sources (RESs) are a risk free investment. The rate of installation of RESs, such as wind turbine (WT) and PV has increased. In India, when comparing to official reports from [1] and [2], a 30% capacity increment of renewable energy is observed for a span of less than a year. PV and WT can be installed in areas with consistent irradiation and wind velocity profile. Remote areas without grid connectivity is benefited with RESs. However,
Email address: [email protected] (Hithu Anand) Preprint submitted to Renewable Energy URL: www.ssn.edu.in ()
May 4, 2018
ACCEPTED MANUSCRIPT
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
RI PT
24
RESs may not be able to meet the load demand completely. So a micro-grid with other fuel based sources of power generation is advisable. Micro-grid basically depend on local distributed energy sources. Cost of power generation in a micro-grid is higher than conventional thermal power generation, hence to economise the operation of micro-grid is of greater importance. Non-linear and discontinuous cost characteristics of fuel based sources opt for meta-heuristic as an appropriate method of optimisation. Complexity of generator scheduling problem increases with real time pricing (RTP). A mixed integer non-linear programming approach to single machine scheduling is presented in [3]. From the scheduling results, a better decision making possibility for the problem is observed. In this paper, anti-predatory particle swarm optimisation (APSO) method is used. In a micro-grid, cost from RESs are comparatively lower, making them as high priority energy sources. However, stochastic nature of renewable energy makes it another challenge under real time operation. Considering stochastic nature into benefit, an RTP strategy of micro-grid is presented in this paper. Swarm intelligence based APSO in optimisation of difficult problem is well experimented. Swarm intelligence and other stochastic methods are gaining popularity. A stochastic dynamic programming using greedy algorithm in a wholesale electricity market scenario is explained in [4]. Energy pricing for electric vehicle charging with RESs and energy storage is discussed. Further, an ant colony optimisation in comparison with PSO for energy management system is explained in [5]. Optimum scheduling of micro energy sources in a micro-grid is analysed, claiming improved performance and cost efficiency.
SC
23
M AN U
22
45
49 50 51 52 53 54 55
AC C
56
D
48
Rural electrification rate is much poor in many countries. World bank estimates, approximately 1.2 billion people do not have regular access to electricity. In Latin America, 33.8 million people live without electricity [6]. In Sub-Saharan Africa, around 600 million people live without using electricity [7]. In Bihar, India 75 million people live without electricity [8]. In Chile,79 locations are identified for rural electrification projects for which micro-grid is advised as feasible solution [6]. In Maharastra India, 329 villages are electrified in view of micro-grid concept [9]. An analysis for micro-grid feasibility in three regions of Senegal with population ranging from 250 to 1500 people in each village is given in [10]. The power demand for the same is identified in a range of 6KW to 45KW. Remote villages where to, the transmission grid extension is costlier, standalone micro-grid is desired. For large population, basic standard of living can be accomplished by micro-grid installation.
TE
47
EP
46
57 58 59
60
61
62
63 64 65 66 67
Effective and economic operation of micro-grid is vital for sustained development. Price-signal based control strategy is an elegant method for overall control of micro-grid. Penetration of RTP is difficult, a study on consumers with high demand response is identified to promote RTP [11]. Various policies on time-based-pricing and possible shifting and shaping of load profile is explained in [12]. Further, stochastic nature of RESs call for dynamic pricing a favourable option. A fast acting demand response is modelled and analysed with alternative models in [13]. Dynamic scheduling with the penetration of 2
ACCEPTED MANUSCRIPT
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
RI PT
73 74
SC
72
M AN U
71
D
70
wind energy with economy and RTP is discussed in [14]. Many possible cases of demand response scenario is explained. Further, a dynamic home energy management (HEM) system considering both load and source side is explained in [15]. An elegant explanation on simple ToU and RTP is given in [16] taking high WT power scenario. Similarly, evaluation of impact of RTP due to wind power generation is given in [17]. Awareness of ToU pricing considering 100 million water heaters installed in residential load is given in [18]. Again, usage based dynamic pricing for a smart grid is given in [19]. A practical approach to pricing based on closed loop approach from supply and demand is explained in [20]. Price based wind power utilisation in a deregulated electricity market is given in [21]. A price-responsive customers in a day-ahead scheduling scenario maximising profit of distribution network operator is discussed in [22]. Price based demand response strategy, in view of smart-grid implementation in a residential area in China is discussed in [23] and [24]. Incentive schemes can be given more importance, a virtual RTP considering consumers’ benefit is given in [25]. Linear and non-linear models on incentive based and price based demand response is explained in [26]. Price based demand response model, considering behaviour analysis of decision makers is given in [27]. Further, Solar powered micro-grid with 9,10 and 15 homes are simulated and a pricing mechanism is explained in [28]. Price based control can reduce consumption as well as emission, CO2 emission considering shifting of load to low price hour is given in [29]. Further, techno-economic evaluation of grid connected PV system, considering reduced carbon emission is analysed with the help of HOMER software in [30]. Model predictive control based RTP for commercial building is presented in [31]. The model claims to have easy control possibility for end-users. A survey on RTP for typical industrial customers in United States is discussed in [32]. Hence from the literature, many approach to incentive schemes with RTP and ToU using RESs pricing is observed. In this paper, a novel method of power-to-cost index based RTP is presented. Power-to-cost index is a measure of power generation from both renewable and non-renewable energy sources to that of cost involved in its generation. Obviously, the cost varies with magnitude of renewable power generation as well as, with non-linear cost characteristics of fuel based sources.
TE
69
EP
68
105
This paper is organised as follows: the economic emission dispatch (EED) problem formulation of micro-grid and methodology used is given in section 2. Various components of the micro-grid is presented in section 3. Section 4 gives discussion on simulation results with pricing strategy followed by conclusion in section 5.
106
2. Economic emission dispatch problem formulation
101
AC C
102 103 104
107 108 109 110
Minimising fuel cost of power generation in distributed generation (DG) meeting the equality and inequality constraints is called economic dispatch (ED). Emission dispatch or EED in a micro-grid is an emerging area of study. Since emission depends on non-linear characteristics of emission function from
3
ACCEPTED MANUSCRIPT
different sources, total minimum cost φ is given by φ = min
Ng X (FCM T , ECM T , FCF C )$/hr i=1
114 115 116
where, Ng is total number of generators FCM T is the fuel cost of MT ECM T is the emission cost of MT FCF C is the fuel cost of FC
SC
112 113
117
119 120 121 122
Load forecasting techniques are well explained in [33]. However, a typical power demand profile as shown in figure 1 is taken. Further, WT and PV generation profile taken is, as shown in figure 2. In the proposed standalone micro-grid, power generated by RESs are not stored and immediate dispatch gives the equality constraints as given below
M AN U
118
Pdh = Pgh + PP V h + PW T h
125 126 127
where, Pdh is the power demand in hth hour Pgh is the power generated by fuel sources in hth hour PP V h is the power generated by PV at hth hour PW T h is the power generated by WT at hth hour
128 129
The inequality constraints are the generation limits of FCs and MTs as given below
TE
130
min max PM Ti ≤ PM Ti ≤ PM Ti
132 133
where, th max min MT, rePM Ti and PM Ti are the minimum and maximum power limits of i spectively. max th PFmin FC, respecCi and PF Ci are the minimum and maximum power limits of i tively.
AC C
134 135
(3)
EP
max PFmin Ci ≤ PF Ci ≤ PF Ci
131
(2)
D
123 124
(1)
RI PT
111
136
137
138 139
140 141 142 143 144
Hence, minimising the objective function of equation 1 meeting the equality constraints in equation 2 and the inequality constraints in equation 3 gives desired EED problem in the micro-grid. 2.1. Methodology A standalone micro-grid is taken as an experimental study and the technology is identified to be an ideal solution for remote areas. It is assumed that, forecast values of power generated from WT and PV is readily available. A typical load curve is taken as ideal reference load curve. Further, transmission 4
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
1load-eps-converted-to.pdf
EP
TE
D
Figure 1: Load demand profile
AC C
2Windsolar-eps-converted-to.pdf
Figure 2: Wind-solar generation profile
5
ACCEPTED MANUSCRIPT
148 149 150 151 152
153 154 155 156 157 158 159 160 161 162 163 164
RI PT
147
losses within the micro-grid and reserve power generation is neglected. Hence, power generation cost is directly reflected as RTP of energy at the consumer end. PSO method of EED is compared with APSO for finding minimum total cost, this in turn is used to find average power-to-cost index value for whole day. Average power-to-cost index for the particular day is identified to be 6.69. The same is shown as dashed lines in figure 3. It is taken as reference for identifying incentive periods while the micro-grid is operating with integrated RESs in real time.
2.1.1. Particle swarm optimisation PSO is one of the popular and simplest method of optimisation for large non-linear problems. It was first introduced by Kennedy and Eberhart in the year 1995. In 2006, anti-predatory particle swarm optimisation was proposed by Immanuel and Thanushkodi [34]. PSO method is inspired from social survival behaviour of birds. Position and velocity of swarm of birds in three dimensional space is modelled into hyper-dimension, to obtain optimal solution for complex higher dimensional real world problems. Many such non-linear problems where solved using swarm intelligence based method. Parallel PSO based search method for solving large scale problem is efficiently applied by Subbaraj et. al. [35]. However, an upgrade to PSO is APSO method, which is used for solving the proposed problem.
SC
146
M AN U
145
165
170 171 172 173 174 175 176
AC C
177
D
169
TE
168
2.1.2. Power-to-cost index Power-to-cost index is the ratio of power generated to cost of power generation at any given hour. This value is obtained from minimum cost of ED without RESs. Hence, from the energy cost obtained for the given load profile, an average value of 6.69 is obtained as power-to-cost index without RESs. It is an index taken as a measure to interpret, the significant change in price of energy under the influence of stochastic RESs. Cost rate of 0.074$/kW for WT and 0.125$/kW for PV is taken from reports by US department of energy (DOE) [36]. Here we take the power generation profile from RESs pre-fixed, even though many approach to sizing of RESs using methods like PSO is available, as in [37]. Since the cost of generation is directly reflected onto customers, direct cost benefit due to RESs is modified into a new pricing strategy. A similar and innovative approach of hybrid energy sizing with a pre-fixed reliability index is also explained in [36].
EP
166 167
178 179 180
181
182 183 184 185 186 187 188
Since EED for DG is a non-linear problem, cost of generation varies with dynamic dispatch. Hence, the average of power-to-cost index for whole day is taken for further calculation. Figure 3 presents typical power-to-cost comparison, index crossing the line of averaged index is considered as incentive period. Whenever the index is greater than the averaged power-to-cost index, an incentive is provided. Incentives motivate customers to reduce energy price at that particular hour. The amount of incentive is a share of benefit due to RESs. Hence, an increase in renewable power generation due to its inherent nature, 6
RI PT
ACCEPTED MANUSCRIPT
SC
3power_to_cost-eps-converted-to.pdf
Figure 3: Power-to-cost index comparison
193 194 195 196 197 198 199 200 201
202 203 204 205
2.1.3. Anti-predatory PSO Anti-Predatory PSO is an upgrade to PSO method. APSO method is inspired from survival behaviour of birds avoiding worst case memories [34]. Flowchart for APSO method of dynamic EED problem with RESs and RTP is shown in figure 4. APSO methodology for energy pricing is given below t−1 t−1 Vijt = wt × Vijt−1 + c1 × r1 × Pbest − Xij ij
t−1 +c2 × r2 × Gt−1 besti − Xij
t−1 t−1 +c3 × r3 × Xij − Pworst i
t−1 +c4 × r4 × Xij − Gt−1 worsti
AC C
206
M AN U
192
D
191
is directly benefited as an increment in incentive. Similarly, cumulative reduction in power consumption also makes the new calculated index higher than averaged index, making the period falls under incentive period. This provision, drives the customers to reduce their consumption, indirectly minimising the excess power demand. Hence, the proposed method is identified to have the potential to accommodate stochastic RESs as well as to put a control over power consumption.To reduce carbon emission is also an important concern of nations. A review on reduced carbon emission with government subsidy is analysed for European countries in [38].It is identified that renewable energy sources from developed countries are competent enough with the proposed scheme. Similar method of impact of renewable energy source installation in residential areas and net metering into sufficient incentive in comparison with a levelized cost of electricity is explained in [39].
TE
190
EP
189
207 208 209 210 211
where, t is the iteration count wt is inertia coefficient at t t−1 Xij is position vector at t-1 t Vij is velocity vector at t 7
(4)
ACCEPTED MANUSCRIPT
Method
Minimum Cost
213 214 215 216 217
218.865 208.542 204.557 192.109
220 221
7.618 6.909 5.525 6.409
Variance
Global hits
58.041 47.736 30.531 41.077
97 95 99 98
where, t Xij is position vector at t t−1 Xij is position vector at t-1 t Vij is velocity vector at t
(5)
D
218
Standard Deviation
Vijt−1 is velocity vector at t-1 c1 , c3 are self best and self worst confidence factor c2 , c4 are swarm best and swarm worst confidence factor t−1 Pbest , Gt−1 bestij are personal and global best at t-1 ij t−1 Pworstij , Gt−1 worstij are personal and global worst at t-1 r1 , r2 , r3 , r4 are random number between 0 and 1 t−1 t Xij = Xij + Vijt
219
241.991 226.24 219.728 214.526
M AN U
212
202.980 192.386 189.874 178.445
Maximum Cost
SC
PSO without RESs PSO with RESs APSO without RESs APSO with RESs
Average Cost
RI PT
Table 1: PSO and APSO Statistics for 100 Trials
TE
222
Statistics for PSO and APSO methods with 100 trials is given in table 1. Confidence parameters c1 , c2 , c3 and c4 are obtained from table 2 and table 3.
225
3. Micro-grid
226 227
Micro-grid can be a complete energy solution for remote areas where power grid extension is costlier. Micro-grid consists of DG including RESs. There are various energy management system (EMS) schemes proposed for such technology. An elegant demonstration of real time EMS for a micro-grid using genetic algorithm is given in [40]. Smart EMS (SEMS) for micro-grid economic operation considering day ahead prediction of PV power with weather condition is proposed in [41]. SEMS having forecasting module, energy storage system management module and optimisation module is implemented. SEMS combined the complex pricing and economic dispatch to a single objective optimisation problem, a matrix real-coded genetic algorithm is used for optimisation. Major challenge in micro-grid operation is stochastic power generation from RESs and coordination between them. Planning a micro-grid with optimal mix of distributed energy sources is given in [42]. Robust optimisation method is adopted considering forecast errors in load, stochastic renewable generation and
AC C
228
EP
224
223
229 230 231 232 233 234 235 236 237 238 239
8
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE
D
4flowchart-eps-converted-to.pdf
Figure 4: Dynamic EED flowchart with energy pricing using APSO
9
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
5uG-eps-converted-to.pdf
Figure 5: Proposed micro-grid layout
243 244 245 246 247 248 249 250 251 252
Block diagram layout for proposed micro-grid is shown in figure 5. Power flow from MT, FC, WT and PV is illustrated along with wide area network (WAN) signals. EMS retrieve the status of fuel based sources and load demand profile. Forecast signals can be used to predict day-ahead power generation profiles of WT and PV. Followed up cost analysis of power generation decides the pricing, this signal is send to customers using WAN in real time.
AC C
253
D
242
market prices. Similarly, robust energy management with worst-case scenario optimisation, considering exchange of electricity price is given in [43]. Problem formulation, minimising operational cost and power loss is explained in [44]. Intermittent nature of RESs along with optimal charging of plug-in electric vehicles, distributed generators and distributed energy storage devices is considered. Managing DGs, finally benefit the utility, customers or both and, a simple energy pricing strategy always gives positive response from customers.
TE
241
EP
240
254
255
256 257
258 259 260
3.1. Micro-turbines MTs with maximum power generation of 30KW is considered as a DG type. Capstone manufacture such MT with natural gas as its fuel [45]. Fuel cost and emission cost of the MT is given as follows FCM T = a × p2M Ti + b × PM Ti + c
where, FCM T is the total fuel cost of MTs a, b, c are the MT cost coefficients
10
(6)
ACCEPTED MANUSCRIPT
261
PM Ti is the power generated by ith MT
262
263 264 265 266 267
where, ECM T is the total emission cost of MTs ECO2i is the carbon dioxide emission coefficient of ith MT ESO2i is the sulphur dioxide emission coefficient of ith MT EN Oxi is the nitrogen oxides emission coefficient of ith MT
270 271 272 273
3.2. Fuel cells FCs with maximum power rating of 30KW is taken for the micro-grid. FC convert hydrogen fuel into electrical energy. They are pollution free however, initial cost of installation is high [45]. Cost of power generation from FC is given by
M AN U
269
SC
268
FCF C = KF Ci × PF Ci 274 275 276 277
(7)
RI PT
ECM T = ECO2i × PM Ti + ESO2i × PM Ti + EN Oxi × PM Ti
(8)
where, FCF C is the total fuel cost of FCs KF Ci is the fuel cost coefficient of ith FC PF Ci is the power generated by ith FC
281 282 283 284
t0
AC C
285
3.3. Renewable energy sources 3.3.1. Photo voltaic Power generated by PV mode depends on the level of irradiation, system efficiency and temperature of geographical area. More research on PV life cycle and economic analysis in the event of uncertainty is given in [46]. Hence power from PV is stochastic in nature. Power generation formulation for PV is given by [47] Z t PP V = AP V × ηP V × I(t) × f (t) × d(t) (9)
TE
280
EP
279
D
278
286 287
288
289
290 291
292
where, PP V is the power generated by PV in MW AP V is the total area of PV panel ηP V is the PV system efficiency I(t) is the hourly irradiation in KW hr/m2 f (t) is the radiance density t0 and t are initial and final time of operation respectively
293
11
RI PT
ACCEPTED MANUSCRIPT
SC
6parameter-eps-converted-to.pdf
Figure 6: APSO confidence parameter interpolation for 100 trials
295 296 297 298
3.3.2. Wind turbine WT is the largest contributors of RES. Power generation from WT depends on geographical area, wind velocity and capacity of WT. More study on various types and characteristics of WT is given in [48]. Power generated from WT is given by [47]
M AN U
294
PW T = 1/2 × ρa × AW T × Cp,W T Z t ×ηW T × va3 × f (t) × d(t)
(10)
302 303 304 305 306
AC C
307
TE
301
where, PW T is the power generated by WT AW T is the WT swept area ρa is the air density, 1.225Kg/m3 Cp,W T is the coefficient of WT performance ηW T is the combined efficiency of WT and generator va is the wind velocity in m/sec f (t) is the wind probability density function
EP
299 300
D
t0
308
309
310 311 312 313 314 315 316
4. Simulation results and pricing strategy Modelling of the proposed micro-grid is explained with a layout, appropriate assumptions and methodology. Stochastic method of APSO is used to minimise the EED problem. With the help of flowchart, EED problem into RTP is deduced. Confidence parameter selection graph with RESs and without RESs is given in figure 6. It is identified that cost decreases to particular parameter and then increases. Hence inferred that, parameter defines the convergence to optimal minimum value in stochastic optimisation. APSO statistics of minimum cost from 1000 iteration for 100 trials without and with RESs is presented in
12
ACCEPTED MANUSCRIPT
c4
Minimum Cost
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
225.107 223.656 221.693 214.311 202.074 198.795 195.461 193.607 192.723 191.324 190.758 192.366 189.874 193.724 193.751
Average Cost 251.05 244.59 238.855 231.546 219.236 210.82 208.052 205.276 203.676 202.831 204.205 205.77 204.557 207.293 210.248
Maximum Cost 271.142 267.563 257.301 252.977 238.09 226.515 219.423 221.496 222.508 217.026 216.446 222.528 219.728 222.872 231.904
SD
Variance
Global hits
9.065 7.767 7.908 7.5 6.993 5.776 5.926 5.654 6.287 5.784 5.629 5.849 5.525 6.289 7.316
82.173 60.328 62.537 56.243 48.901 33.363 35.113 31.97 39.521 33.451 31.684 34.211 30.531 39.553 53.529
81 96 98 98 94 100 100 100 99 100 100 99 99 99 93
SD
Variance
Global hits
8.784 8.839 7.078 7.847 7.373 6.248 6.523 6.555 5.165 5.582 6.409 6.402 6.262 6.442 7
77.167 78.122 50.095 61.582 54.354 39.032 37.219 42.963 26.68 31.153 41.077 40.983 39.219 41.495 48.993
73 74 95 93 98 100 96 95 99 98 97 97 96 89 91
SC
c3
M AN U
c2
D
c1
RI PT
Table 2: APSO Parameter Selection for 100 Trials without RESs
c3
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
AC C
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
c2
c4
EP
c1
TE
Table 3: APSO Parameter Selection for 100 Trials with RESs
.05 .04 .03 .02 .01 .005 .004 .003 .002 .001 .0005 .0004 .0003 .0002 .0001
Minimum Cost
211.66 207.897 208.203 204.137 194.524 187.002 180.739 178.996 182.172 179.528 178.445 180.763 179.245 180.295 182.803
Average Cost 236.786 231.324 227.368 219.101 207.876 199.1 196.012 192.875 192.159 190.711 192.109 193.02 193.723 197.556 199.945
13
Maximum Cost 253.2 257.389 244.754 236.032 231.252 213.709 217.868 210.732 210.442 207.761 214.526 215.518 215.596 212.294 219.351
ACCEPTED MANUSCRIPT
318 319 320 321
table 2 and table 3 respectively. Global hits are identified from 15% of minimum cost from 100 trials. For the same minimum cost, dynamic EED schedule without and with RESs is presented in table 4 and table 5 respectively. Overall power generation meeting load demand with RESs using APSO is graphically represented in figure 7.
RI PT
317
322
328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350
Uncertainty scenario analysis for various time period on the same day is carried out. For a 10% increase in load demand at 3AM, 5AM, 8AM, 5PM and 7PM, the RTP is shown in figure 9. Similarly, for a 10% decrease in load demand is shown in figure 10. From both the case studies, it is identified that, new power-to-cost index varies as expected to that of set value of 6.69. That is, for increased load demand at 5PM, the index decrease to 8.98 to the initial case of 9.69 and, for decreased load demand, the index increases to 13.94 to that of 9.69. Note that falling of new power-to-cost index below the set value might have nullified the incentive at that particular hour. However, the renewable power generation is so high, so as to keep the period within incentive period. From tabulation 7 and 8 , the RTP of 2.919$ to that of 4.665$ in the initial case and 4.786$ with RESs is observed. Hence, a price-signal send to reduce energy consumption to 10% will dramatically reduce the fuel cost and followed emission cost by proposed RTP at the utility side. Similar techniques of sending simple message for reduced consumption and load shifting is given in [16].
AC C
351
SC
327
M AN U
326
D
325
With power-to-cost index, an awareness is brought towards reduction in pricing by virtue of low cost power from RESs. More explanation for the proposed power-to-cost index is given in section 2. Once customers become aware of power-to-cost index, it can be used as a bias towards reduced energy consumption. Signal from smart-meter can also be shared to personal devices (cellphone) for increased awareness and followed up decision. Customers will be able to monitor anticipated energy price for next hour. That is by broadcasting a choice to bring down price within the incentive period by virtue of reduced energy consumption. A smart plug compatible with such an operation is discussed in [49]. Dynamic coordination among home appliances to turn them ON or OFF under various demand response scenario is presented in [50]. An optimal scheduling using home energy management technique is obtained. Overall price profile comparison is given in table 6, the same is shown in figure 8. Note that for many instants, pricing without RESs are higher than pricing with RESs. Pricing with RESs will not make significant bias towards reduction in consumption of energy. Similarly, for some instants price control with RESs is higher than pricing with RESs however, it is never greater than pricing without RESs. Hence, price control with RESs is attractive as it adds a control feature apart from RTP for the same daily total price. Price control strategy is achieved with incentives and keeping power-to-cost index as benchmark. Assuming immediate usage of power from RESs, resultant reduction in power from fuel based sources reduce cost of generation.
TE
324
EP
323
352 353 354 355 356 357 358 359 360 361
14
Table 4: Dispatch Schedule without RESs
Load, W
M T1
M T2
M T3
M T4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
32080 26803 24109 22313 18047 18047 26803 35897 48471 47460 46787 54196 54645 49481 48246 45103 45215 62953 80690 87875 88998 80578 68005 49481
355 185 37 53 143 521 319 447 131 19 2 573 95 1177 711 88 1011 777 53 710 912 355 34 1145
355 185 37 53 143 521 319 4777 5124 19 2 5612 3588 1172 5676 5435 11189 688 11611 13703 912 355 3228 1145
355 185 37 53 143 521 1084 1160 3357 7046 2 574 3487 4993 5245 1187 88 777 1930 5477 11971 6772 7192 2716
5179 185 37 53 143 521 271 447 131 2563 2 4912 5999 10300 3754 88 88 23755 15033 10156 19064 19525 18946 1231
F C1
F C2
Cost, $/hr
3355 3133 3052 3090 3131 3500 3270 3389 10721 8142 24443 12638 12125 4091 7086 8341 3096 7056 22858 27910 26204 24555 9121 13873
22481 22930 20909 19011 14344 12463 21540 25677 29007 29671 22336 29887 29351 27748 25774 29964 29743 29900 29205 29919 29935 29016 29484 29371
4.078 2.933 2.659 2.633 2.626 3.164 3.296 4.836 7.176 6.592 7.863 8.208 8.166 7.667 7.942 6.086 7.088 11.268 15.344 17.492 17.904 15.306 12.073 7.473
D
M AN U
SC
Hour
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE
Total
7gen_load-eps-converted-to.pdf
Figure 7: Overall generation meeting load demand with RESs using APSO
15
189.874
ACCEPTED MANUSCRIPT
RI PT
Table 5: Dispatch Schedule with RESs
M T1
M T2
M T3
M T4
F C1
F C2
WT
PV
Cost, $/hr
32080 26803 24109 22313 18047 18047 26803 35897 48471 47460 46787 54196 54645 49481 48246 45103 45215 62953 80690 87875 88998 80578 68005 49481
236 196 402 619 557 310 587 154 44 204 930 50 77 134 84 974 181 106 54 17 5 473 906 32
282 196 402 619 557 310 587 154 44 2828 930 50 77 134 84 2269 181 2355 6768 11928 1934 473 906 2441
300 196 402 619 557 310 587 154 3807 204 930 50 6552 243 84 5453 181 3051 12904 3095 6563 8917 10353 32
236 7022 402 619 557 25 587 154 9524 2893 6858 15985 6475 9376 4786 974 3549 17251 7132 9447 20646 23015 10580 10140
3279 3156 3378 3622 3514 3295 3491 3133 3078 7191 14392 3059 5868 3156 13921 3889 4087 6329 17553 29608 28550 18587 14825 7439
27663 15339 18973 16083 9801 11266 17733 24139 27436 28757 15938 28150 29763 28858 23441 25764 29264 28938 29214 29608 28813 27185 29457 29115
84 698 150 132 2504 2531 3231 8000 3874 3420 3575 2426 833 2585 1241 1797 4575 2738 6086 3970 2487 1928 978 282
0 0 0 0 0 0 0 9 664 1963 3234 4426 5000 4995 4605 3983 3197 2185 979 202 0 0 0 0
3.21 4.083 3.177 3.441 3.322 2.939 3.643 3.518 6.182 5.784 8.285 7.375 7.404 5.758 6.949 6.528 4.665 9.81 14.437 16.182 16.983 16.059 12.004 6.707
D
M AN U
SC
Load, W
178.445
EP
TE
Total
Table 6: Pricing strategy comparison tabulation
1
2
3
4
5
6
7
8
9
10
11
12
Price without RESs Price with RESs Price control with RESs
4.078 3.21 3.505
2.933 4.083 2.933
2.659 3.177 2.285
2.633 3.441 2.633
2.626 3.322 2.626
3.164 2.939 3.164
3.296 3.643 2.833
4.836 3.518 4.156
7.176 6.182 6.167
6.592 5.784 5.665
7.863 8.285 7.863
8.208 7.375 7.053
Hour
13
14
15
16
17
18
19
20
21
22
23
24
Price without RESs Price with RESs Price control with RESs
8.166 7.404 7.018
7.667 5.758 6.589
7.942 6.949 6.825
6.086 6.528 5.23
7.088 4.665 6.091
11.268 9.81 11.268
15.344 14.437 15.344
17.492 16.182 17.492
17.904 16.983 17.904
15.306 16.059 15.306
12.073 12.004 12.073
7.473 6.707 6.422
AC C
Hour
16
RI PT
ACCEPTED MANUSCRIPT
M AN U
SC
8price-eps-converted-to.pdf
Figure 8: Price profile comparison diagram
Table 7: Dispatch Schedule with and without RESs under Uncertainty of 10 % Increment in Load
Load, W
M T1
M T2
3 3 5 5 8 8 17 17 19 19
26520 26520 19851 19851 39486 39486 49737 49737 88760 88760
3 2 3 15 214 6 113 250 145 920
3 2 3 15 214 6 938 250 4387 4224
M T3
M T4
F C1
F C2
WT
PV
Cost, $/hr
3 2 3 15 214 6 113 250 6161 4910
3 2 3 15 5785 6 17683 8098 18323 12991
3002 3002 3004 3014 3193 3008 3692 3224 29751 29325
23506 23360 16835 14274 29867 28446 27191 29892 29993 29325
0 150 0 2504 0 8000 0 4575 0 6086
0 0 0 0 0 9 0 3197 0 979
2.678 2.683 2.483 2.610 4.216 3.418 7.609 4.786 16.980 15.831
AC C
EP
TE
D
Hour
9price2-eps-converted-to.pdf
Figure 9: RTP with 10% load increment at various hours
17
RI PT
ACCEPTED MANUSCRIPT
Table 8: Dispatch Schedule with and without RESs under Uncertainty of 10 % Decrement in Load
M T1
M T2
M T3
M T4
3 3 5 5 8 8 17 17 19 19
21698 21698 16242 16242 32307 32307 40694 40694 72621 72621
5 2 3 2 10 7 135 41 36 412
5 2 3 2 10 7 135 41 2653 412
5 2 3 2 10 7 135 41 3072 412
5 2 3 2 10 7 3891 42 12067 10204
F C1
F C2
SC
Load, W
3003 3005 3002 3004 3009 3006 6425 3037 25216 24780
18675 18535 13228 10726 29254 21264 29972 29719 29577 29336
EP
TE
D
M AN U
Hour
AC C
10price3-eps-converted-to.pdf
Figure 10: RTP with 10% load decrement at various hours
18
WT
PV
Cost, $/hr
0 150 0 2504 0 8000 0 4575 0 6086
0 0 0 0 0 9 0 3197 0 979
2.539 2.542 2.376 2.487 2.857 3.208 4.529 2.919 12.425 11.300
ACCEPTED MANUSCRIPT
364 365 366 367 368 369 370 371 372 373
In this paper, dynamic EED in a typical micro-grid is presented. EMS module is assigned to coordinate generation as well as pricing strategy. It is identified that availability of power generation profile from stochastic WT and PV contributed in scheming dynamic EED. It is also inferred that APSO outperform PSO method in economising dynamic EED by 7.24% with RESs. Finally, new pricing strategy with power-to-cost index is presented focusing on stochastic nature of RESs. In a realistic scenario, smart-meters compatible of receiving WAN (WiMAX) signal is considered to be a feasible option for monitoring the RTP. Further, the method with behavioural response is identified to lower the excess power demand, hence resulting to peak demand reduction, lower generation cost, reduced emission and economy within the micro-grid.
RI PT
363
5. Conclusion
SC
362
378
379 380 381 382 383 384
385 386 387
388 389
[2] S. Chandel, R. Shrivastva, V. Sharma, P. Ramasamy, Overview of the initiatives in renewable energy sector under the national action plan on climate change in India, Renewable and Sustainable Energy Reviews 54 (2016) 866–873. doi:10.1016/j.rser.2015.10.057. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032115011363 [3] S. Lee, B. Do Chung, H. W. Jeon, J. Chang, A dynamic control approach for energy-efficient production scheduling on a single machine under timevarying electricity pricing 165 (2017) 552–563. [4] C. Luo, Y.-F. Huang, V. Gupta, Stochastic dynamic pricing for EV charging stations with renewables integration and energy storage 9 (2017) 1494 – 1505.
AC C
390
[1] Executive Summary of Power Sector - March 2016, central electricity authority (cea) (2016). URL http://www.cea.nic.in/monthlyexesummary.html
D
377
TE
376
6. References
EP
375
M AN U
374
391
392 393
394 395
396 397
398 399 400
[5] M. Marzband, E. Yousefnejad, A. Sumper, J. L. DomnguezGarca, Real time experimental implementation of optimum energy management system in standalone microgrid by using multilayer ant colony optimization 75 (2016) 265 – 274. doi:https: //doi.org/10.1016/j.ijepes.2015.09.010. URL http://www.sciencedirect.com/science/article/pii/ S0142061515003920
[6] It Takes a Village: Social SCADA and Approaches to Community Engagement in Isolated Microgrids, IEEE Power and Energy Magazine 12 (4) (2014) 60–69. doi:10.1109/MPE.2014.2317419.
19
ACCEPTED MANUSCRIPT
409 410 411 412 413
414 415 416 417 418 419
420 421 422 423 424 425
426 427 428
429 430
[8] L. Molyneaux, L. Wagner, J. Foster, Rural electrification in India: Galilee Basin coal versus decentralised renewable energy micro grids, Renewable Energy 89 (2016) 422–436. doi:10.1016/j.renene.2015.12.002. URL http://linkinghub.elsevier.com/retrieve/pii/ S0960148115305061
[9] R. Bhoyar, S. Bharatkar, Potential of MicroSources, Renewable Energy sources and Application of Microgrids in Rural areas of Maharashtra State India, Energy Procedia 14 (2012) 2012–2018. doi:10.1016/j.egypro.2011.12.1202. URL http://linkinghub.elsevier.com/retrieve/pii/ S1876610211046224 [10] H. Camblong, J. Sarr, A. Niang, O. Curea, J. Alzola, E. Sylla, M. Santos, Micro-grids project, Part 1: Analysis of rural electrification with high content of renewable energy sources in Senegal, Renewable Energy 34 (10) (2009) 2141–2150. doi:10.1016/j.renene.2009.01.015. URL http://linkinghub.elsevier.com/retrieve/pii/ S0960148109000548 [11] G. Wang, Q. Zhang, H. Li, Y. Li, S. Chen, The impact of social network on the adoption of real-time electricity pricing mechanism 142 (2017) 3154– 3159. [12] C. Eid, E. Koliou, M. Valles, J. Reneses, R. Hakvoort, Time-based pricing and electricity demand response: Existing barriers and next steps 40 (2016) 15 – 25. doi:https://doi.org/10.1016/j.jup.2016.04.001. URL http://www.sciencedirect.com/science/article/pii/ S0957178716300947
AC C
431
RI PT
408
SC
407
M AN U
406
D
405
[7] K. Lee, E. Brewer, C. Christiano, F. Meyo, E. Miguel, M. Podolsky, J. Rosa, C. Wolfram, Electrification for Under Grid households in Rural Kenya, Development Engineering 1 (2016) 26–35. doi:10.1016/j.deveng.2015.12.001. URL http://linkinghub.elsevier.com/retrieve/pii/ S235272851530035X
TE
403 404
URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6832818
EP
401 402
432 433
434
435 436
437 438
439 440 441
[13] D. P. Chassin, D. Rondeau, Aggregate modeling of fast-acting demand response and control under real-time pricing 181 (Supplement C) (2016) 288 – 298. doi:https://doi.org/10.1016/j.apenergy.2016.08.071. URL http://www.sciencedirect.com/science/article/pii/ S0306261916311588 [14] Y. Zhai, K. Biel, F. Zhao, J. W. Sutherland, Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real time electricity pricing 66 (1) (2017) 41–44. 20
ACCEPTED MANUSCRIPT
449
450 451 452 453 454
455 456 457 458 459 460
461 462 463 464 465
466 467
468 469 470 471
[17] R. Sioshansi, W. Short, Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation, IEEE Transactions on Power Systems 24 (2) (2009) 516–524. doi:10.1109/TPWRS.2008.2012184. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=4783090
[18] J. J. Shah, M. C. Nielsen, T. S. Shaffer, R. L. Fittro, Cost-Optimal Consumption-Aware Electric Water Heating Via Thermal Storage Under Time-of-Use Pricing, IEEE Transactions on Smart Grid (2015) 1–8doi:10.1109/TSG.2015.2483502. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=7353201 [19] X. Liang, X. Li, R. Lu, X. Lin, X. Shen, UDP: Usage-Based Dynamic Pricing With Privacy Preservation for Smart Grid, IEEE Transactions on Smart Grid 4 (1) (2013) 141–150. doi:10.1109/TSG.2012.2228240. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6471882 [20] J. He, C. Zhao, L. Cai, P. Cheng, L. Shi, Practical closed-loop dynamic pricing in smart grid for supply and demand balancing 89 (2018) 92–102. [21] Q. Wang, J. Wang, Y. Guan, Price-Based Unit Commitment With Wind Power Utilization Constraints, IEEE Transactions on Power Systems 28 (3) (2013) 2718–2726. doi:10.1109/TPWRS.2012.2231968. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6401219
AC C
472
RI PT
448
SC
447
[16] P. Grnewald, E. McKenna, M. Thomson, Keep it simple: time-of-use tariffs in high-wind scenarios, IET Renewable Power Generation 9 (2) (2015) 176–183. doi:10.1049/iet-rpg.2014.0031. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2014.0031
M AN U
445 446
D
444
[15] O. Elma, A. Tackaraolu, A. T. nce, U. S. Selamoullar, Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts 134 (2017) 206–220.
TE
443
EP
442
473
474
475 476 477
478 479
480 481
[22] M. Mazidi, H. Monsef, P. Siano, Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks 115 (2016) 103 – 116. doi:https://doi.org/10.1016/j.enconman.2016.02.030. URL http://www.sciencedirect.com/science/article/pii/ S0196890416300541
[23] C. Yang, C. Meng, K. Zhou, Residential electricity pricing in china: The context of price-based demand response 81 (2017) 2870–2878. [24] H. Wang, H. Fang, X. Yu, S. Liang, How real time pricing modifies chinese households electricity consumption 178 (2017) 776–790. 21
ACCEPTED MANUSCRIPT
489 490 491 492 493 494
495 496 497 498 499
500 501 502 503 504
505 506 507 508 509 510
511
[27] S. Sekizaki, I. Nishizaki, T. Hayashida, Electricity retail market model with flexible price settings and elastic price-based demand responses by consumers in distribution network 81 (2016) 371 – 386. doi:https://doi.org/10.1016/j.ijepes.2016.02.029. URL http://www.sciencedirect.com/science/article/pii/ S0142061516302915
[28] S. Krishnamurthy, R. Tzoneva, Multi Objective Dispatch Problem with Valve Point Effect Loading of Fuel Cost and Emission Criterion, International Journal of Computer and Electrical Engineering 4 (5) (2012) 775. URL http://search.proquest.com/openview/ d114e7a7567a891236a384da7dbcfd87/1?pq-origsite=gscholar [29] T. Hahn, Z. Tan, W. Ko, Design of Time-Varying Rate Considering CO2 Emission, IEEE Transactions on Smart Grid 4 (1) (2013) 383–389. doi:10.1109/TSG.2012.2234771. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6449340 [30] V. Tomar, G. Tiwari, Techno-economic evaluation of grid connected PV system for households with feed in tariff and time of day tariff regulation in new delhi a sustainable approach 70 (2017) 822–835. doi:10.1016/j.rser.2016.11.263. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032116310498 [31] N. T. Mbungu, R. M. Naidoo, R. C. Bansal, Real-time electricity pricing: TOU-MPC based energy management for commercial buildings 105 (2017) 3419–3424.
AC C
512
RI PT
488
SC
487
M AN U
486
[26] M. Rahmani-andebili, Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets 132 (2016) 115 – 124. doi:https://doi.org/10.1016/j.epsr.2015.11.006. URL http://www.sciencedirect.com/science/article/pii/ S0378779615003326
D
484 485
[25] J. Ji, M. Sun, B. C. Ampimah, Design of virtual real-time pricing model based on power credit 142 (2017) 2669–2676.
TE
483
EP
482
513
514
515
516
517 518 519
520 521
[32] N. Nezamoddini, Y. Wang, Real-time electricity pricing for industrial customers: Survey and case studies in the united states 195 (2017) 1023–1037. [33] A. R. Khan, A. Mahmood, A. Safdar, Z. A. Khan, N. A. Khan, Load forecasting, dynamic pricing and dsm in smart grid: A review, Renewable and Sustainable Energy Reviews 54 (Supplement C) (2016) 1311 – 1322. doi:https://doi.org/10.1016/j.rser.2015.10.117. URL http://www.sciencedirect.com/science/article/pii/ S136403211501196X
22
ACCEPTED MANUSCRIPT
528 529 530 531 532 533
534 535 536 537
538 539 540 541 542
543 544 545 546 547
548 549 550 551
[35] P. Subbaraj, R. Rengaraj, S. Salivahanan, T. Senthilkumar, Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem 32 (9) (2010) 1014–1023. doi:10.1016/j.ijepes.2010.02.003. URL http://linkinghub.elsevier.com/retrieve/pii/ S0142061510000566 [36] Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2015 (2015). URL https://www.eia.gov/forecasts/archive/aeo15/pdf/ electricity_generation_2015.pdf [37] M. A. Mohamed, A. M. Eltamaly, A. I. Alolah, Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems 77 (2017) 515–524. doi:10.1016/j.rser.2017.04.048. URL http://linkinghub.elsevier.com/retrieve/pii/ S1364032117305506 [38] B. Bakhtyar, A. Fudholi, K. Hassan, M. Azam, C. Lim, N. Chan, K. Sopian, Review of CO2 price in europe using feed-in tariff rates 69 (2017) 685–691. doi:10.1016/j.rser.2016.11.146. URL http://linkinghub.elsevier.com/retrieve/pii/ S136403211630911X [39] S. Comello, S. Reichelstein, Cost competitiveness of residential solar PV: The impact of net metering restrictions 75 (2017) 46–57. doi:10.1016/j.rser.2016.10.050. URL http://linkinghub.elsevier.com/retrieve/pii/ S136403211630702X
AC C
552
RI PT
527
SC
526
M AN U
525
D
524
[34] A. I. Selvakumar, K. Thanushkodi, Anti-predatory particle swarm optimization: Solution to nonconvex economic dispatch problems, Electric Power Systems Research 78 (1) (2008) 2–10. doi:10.1016/j.epsr.2006.12.001. URL http://linkinghub.elsevier.com/retrieve/pii/ S0378779606002914
TE
523
EP
522
553
554 555
556 557
558
559 560 561 562
[40] M. Elsied, A. Oukaour, T. Youssef, H. Gualous, O. Mohammed, An advanced real time energy management system for microgrids 114 (2016) 742 – 752. doi:https://doi.org/10.1016/j.energy.2016.08.048. URL http://www.sciencedirect.com/science/article/pii/ S0360544216311616 [41] C. Chen, S. Duan, T. Cai, B. Liu, G. Hu, Smart energy management system for optimal microgrid economic operation, IET Renewable Power Generation 5 (3) (2011) 258. doi:10.1049/iet-rpg.2010.0052. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2010.0052 23
ACCEPTED MANUSCRIPT
571 572
573 574 575 576 577
578 579 580 581 582 583
584 585 586 587 588 589
590 591 592 593
[44] W. Su, J. Wang, J. Roh, Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources, IEEE Transactions on Smart Grid 5 (4) (2014) 1876–1883. doi:10.1109/TSG.2013.2280645. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=6662462 [45] R. Lasseter, A. Akhil, C. Marnay, J. Stephens, J. Dagle, R. Guttromson, A. Meliopoulous, R. Yinger, J. Eto, The CERTS microgrid concept, White paper for Transmission Reliability Program, Office of Power Technologies, US Department of Energy 2 (3) (2002) 30. URL http://www.westernsunsystems.comorwww.gosolarcalifornia. org/research/notices/2002-05-02_WORKSHOP_SUPP.PDF [46] S. A. Kalogirou, Chapter 12 - solar economic analysis, in: S. A. Kalogirou (Ed.), Solar Energy Engineering (Second Edition), second edition Edition, Academic Press, Boston, 2014, pp. 701 – 734. doi:https://doi.org/10.1016/B978-0-12-397270-5.00012-1. URL http://www.sciencedirect.com/science/article/pii/ B9780123972705000121 [47] K. Kusakana, Optimal scheduled power flow for distributed photovoltaic/wind/diesel generators with battery storage system 9 (8) (2015) 916–924. doi:10.1049/iet-rpg.2015.0027. URL http://digital-library.theiet.org/content/journals/10. 1049/iet-rpg.2015.0027
AC C
594
RI PT
570
[43] Y. Xiang, J. Liu, Y. Liu, Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load, IEEE Transactions on Smart Grid (2015) 1–1doi:10.1109/TSG.2014.2385801. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=7010943
SC
568 569
M AN U
567
D
565 566
[42] A. Khodaei, S. Bahramirad, M. Shahidehpour, Microgrid planning under uncertainty, IEEE Transactions on Power Systems 30 (5) (2015) 2417– 2425. URL http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber= 6920097
TE
564
EP
563
595
596 597 598
599 600
601 602 603
[48] S. A. Kalogirou, Chapter 13 - wind energy systems, in: S. A. Kalogirou (Ed.), Solar Energy Engineering (Second Edition), second edition Edition, Academic Press, Boston, 2014, pp. 735 – 762. doi:https://doi.org/10.1016/B978-0-12-397270-5.00013-3. URL http://www.sciencedirect.com/science/article/pii/ B9780123972705000133 [49] V. P. Ramavarapu, R. Sowers, R. S. Sreenivas, A smart power outlet for electric devices that can benefit from real-time pricing (2017) 11–17doi: 10.1109/ICCEREC.2017.8226673. 24
ACCEPTED MANUSCRIPT
605 606
AC C
EP
TE
D
M AN U
SC
607
[50] A. Khalid, N. Javaid, M. Guizani, M. Alhussein, K. Aurangzeb, M. Ilahi, Towards dynamic coordination among home appliances using multiobjective energy optimization for demand side management in smart buildings 99 (2018) 1–1.
RI PT
604
25
TE
D
M AN U
TE
D
M AN U
TE
D
M AN U
CE
ED
PT M AN US
C
ED
PT
M AN US
TE
D
M AN U
TE
D
M AN U
ED
PT
M AN US
TE
D
M AN
TE
D
M AN
ACCEPTED MANUSCRIPT Research Highlights:
Power-to-cost index is proposed as a real time pricing strategy.
•
Remote/ standalone micro-grid is taken as a case study.
•
Wind and solar energy into pricing is considered.
•
Economic emission dispatch with numerical results for single day is presented.
•
Claims better control to lower peak demand, generation cost and emission cost.
AC C
EP
TE D
M AN U
SC
RI PT
•