Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis

Energy xxx (2014) 1e10

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Decentralized demandesupply matching using community microgrids and consumer demand response: A scenario analysis Kumudhini Ravindra*, Parameshwar P. Iyer Department of Management Studies, Indian Institute of Science, Bangalore, India

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 October 2013 Received in revised form 25 January 2014 Accepted 11 February 2014 Available online xxx

Developing countries constantly face the challenge of reliably matching electricity supply to increasing consumer demand. The traditional policy decisions of increasing supply and reducing demand centrally, by building new power plants and/or load shedding, have been insufficient. Locally installed microgrids along with consumer demand response can be suitable decentralized options to augment the centralized grid based systems and plug the demandesupply gap. The objectives of this paper are to: (1) develop a framework to identify the appropriate decentralized energy options for demandesupply matching within a community, and, (2) determine which of these options can suitably plug the existing demand esupply gap at varying levels of grid unavailability. A scenario analysis framework is developed to identify and assess the impact of different decentralized energy options at a community level and demonstrated for a typical urban residential community e Vijayanagar, Bangalore in India. A combination of LPG based CHP microgrid and proactive demand response by the community is the appropriate option that enables the Vijayanagar community to meet its energy needs 24/7 in a reliable, cost-effective manner. The paper concludes with an enumeration of the barriers and feasible strategies for the implementation of community microgrids in India based on stakeholder inputs. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Scenario analysis Community microgrids Consumer demand response Energy policy Electricity demandesupply matching India

1. Introduction Energy forms a vital input and critical infrastructure for the economic development of countries and for improving the quality of life of people. In a growing world, as the focus shifts to better access and use of modern energy sources, there is a rising demand for energy. Utilities in developing countries constantly face the challenge of reliably matching energy (specifically electricity) supply to this increasing consumer demand. Demandesupply matching at a utility level involves four kinds of basic decision options e (1) increasing supply centrally, (2) reducing demand centrally, (3) increasing supply locally, and (4) reducing demand locally. The traditional response of utilities to increasing demand for power has been to increase supply centrally, in the long term, by building new power plants or to reduce demand centrally, in the short term, by load shedding. However these centralized responses are not adequate in plugging the increasing

* Corresponding author. E-mail addresses: [email protected], [email protected] (K. Ravindra), [email protected] (P.P. Iyer).

electricity demandesupply gap. Microgrids are entities that comprise a LV (z
1 kV) or MV (usually z1e69 kV) locally controlled cluster of DERs (Distributed Energy Resources) that behave, from the grid’s perspective, as a single producer or load both electrically and in energy markets. Such microgrids, using decentralized renewable and clean energy resources with or without consumer demand response, can be suitable options to augment the centralized grid based systems. These microgrid systems enable demandesupply matching at a community or even individual consumer level. They provide customized local solutions to the demandesupply problem at a consumer level. The objectives of this paper are: (1) to develop a framework to help identify the appropriate decentralized energy options for demandesupply matching in a community that can be used along with grid power, and, (2) to determine which of these options can suitably plug the demandesupply gap existing in a specific community due to varying levels of grid unavailability. To address these objectives a scenario analysis framework is developed to identify and assess the impact of different

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Please cite this article in press as: Ravindra K, Iyer PP, Decentralized demandesupply matching using community microgrids and consumer demand response: A scenario analysis, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.02.043

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decentralized energy options at a community level and demonstrated for a typical urban residential community e Vijayanagar, Bangalore in India. 2. Scenario analysis methodology Scenarios are hypothetical and describe possible future pathways [1]. They describe dynamic processes, representing sequences of events over a period of time. They consist of states, driving forces, events, consequences and actions that are causally related. They start from an initial state (usually the present) and then depict a final state at a fixed time horizon [2,3]. The purpose of scenarios [4,5] is to: (i) change thinking and create a common vision, (ii) decision support, (iii) manage risk and uncertainty, and (iv) learn and understand. Scenario narratives [6] provide ‘‘texture, richness, and insight’’ while models offer a level of “structure, discipline, and rigor to the analyses of socioeconomic, resource, and environmental conditions”. Scenarios do not try to account for every possible outcome rather they focus on developing a set of insightful lenses for exploring processes of change. Scenarios fill an important gap in explaining and depicting options for decision makers [7,8]. Areas of application include urban development, energy system and transport planning, planning of education, health services and other public service systems, and sustainable development. Scenarios are used widely in public policy and energy planning [9e14]. The use of scenarios in public policy making follows two key dimensions, namely (1) to engage external stakeholders in decision-making and (2) their use internally within the public sector. In addition, policy makers use scenarios for the concrete purpose of decision support [15]. The scenario analysis process in this study develops scenarios in relation to the implementation of community microgrids. It follows the six-step process for developing scenarios proposed by Schwartz [16,17], which includes the following steps: 1. 2. 3. 4. 5. 6.

Define a focal issue List important forces in the environment Evaluate forces by importance and uncertainty Select a scenario logic Develop scenarios around critical uncertainties Evaluate the implications of the scenarios

In the study, the scenario analysis framework is divided into 3 stages (as shown in Fig. 1). The first 3 steps of the scenario analysis six-step process are part of Stage 1. It involves defining the scenarios. The Stage 1 in this study is a stakeholder workshop. Stakeholders are key actors in the implementation of the scenarios. In order to ensure a participatory mode of decision-

making, the initial scenario identification process is performed with representatives of stakeholders. A stakeholder workshop [18,19] was held with representatives of the different stakeholder groups viz., the power generation, transmission and distribution utilities, the independent power producers and energy service providers, consumers and consumer advocacy groups, regulators, government, NGOs and other linked institutions like water supply utility, city and state planning commission. Over 60 participants over two days deliberated on the following aims. They were to: (1) identify the needs of today’s energy systems, (2) identify the key issues for today’s energy systems and select the main issue for the scenarios to focus on, (3) enumerate and select different clean and renewable energy sources that are suitable for decentralized generation in the selected community and (4) identify potential barriers for the implementation of creation of decentralized energy systems. The workshop also included a focus group discussion with experts to further consolidate the identified issues and rank them. The second stage in the scenario analysis framework is Scenario Development. This includes steps 4 and 5 of the six-step process. The aim of Stage II is to evaluate issues identified by the stakeholders, select the scenario logic and develop scenarios around critical uncertainties. The third stage in the scenario analysis framework is Scenario Evaluation. This stage consists of step 6 of the six-step process. The purpose of Stage III is to evaluate the scenarios based on their possible effects and implications.

3. Scenario analysis 3.1. .1Results from the stakeholder workshop Step 1 of the six-step process is definition of the focal issue. The focal issue poses a set of questions that decision makers/policy makers face about clean energy and possible alternative pathways. From the stakeholder workshop, the focal issues identified are: 1. How do we address the demandesupply gap at community level in India? 2. What will be the drivers and barriers of change over the next decades? 3. How could the new energy systems based on microgrids differ from business-as-usual? Focus Group Discussion [18,19] and literature [20e24] identify the several drivers for the management of energy demandesupply and implementation of microgrids and demand side management. The key forces of impact in India are:

Fig. 1. Stages of scenario analysis framework with inputseoutputs.

Please cite this article in press as: Ravindra K, Iyer PP, Decentralized demandesupply matching using community microgrids and consumer demand response: A scenario analysis, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.02.043

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Fig. 2. Existing land use of Vijayanagar, Bangalore, India, (source: BDA, 2007).

i. ii. iii. iv. v. vi. vii.

perception of distributed generation benefits/costs, facility of interconnection for distributed generation, fuel price, alternatives price and supply volatility versus stability, relevance of energy diversity, long-term prominence of energy and security, and agents of leadership.

The scenario logic chain is based on the central question of the existence of a demandesupply gap at community level and how it can be addressed. Demandesupply matching can be theoretically and practically addressed by several means. The first option is to increase supply. This can be done either centrally or locally. The second option is to reduce demand. This too can be done either centrally or locally. Traditional demandeSupply matching has been achieved by increasing supply centrally. However, it can be seen that grid electricity cannot keep pace with the growing demand. Also increasing supply centrally does not necessarily ensure security of supply to consumers at community level as the Utility follows a priority list for electricity supply and domestic consumers do not usually rank high on the list. The demand reduction at the central level is achieved by means of load shedding, which has been proven to an economically unviable proposition [18]. For the purpose of this study, only local increase of supply through use of microgrids and local reduction of demand through use of demand response is considered. Impact of possible changes in technology, values, social and lifestyle are also examined. In all, objectives of four major stakeholders (actors) [24] are considered for the scenario analysis. They are the Electricity Utility, consumer/community, society and the Government. The objective

of the Electricity Utility is economic viz., profit maximization for the organization in a technically reliable and cost effective manner. The objective of the Government is assumed to be social viz., ensuring access to all consumers in the community along with enabling affordable power for all consumers and/or increased employment generation. The objective of the society is seen through the lens of the NGOs (Non-governmental organizations) and is assumed to be environmental. It includes GHG abatement or reduction of emissions. Finally the objective of the consumer or community is increase of their personal security of supply. Consumers basically require reliable 24/7 electricity, to meet their energy needs. These objectives are conflicting in nature and it has been established that optimal solutions considering only economic objectives may not be the best solution. What are necessary are satisfying solutions that consider all these objectives [18]. Stakeholder objectives are implicitly incorporated into the scenarios in the present study.

3.2. Data for the scenario development In this study, data for the scenario analysis is from an urban community in Bangalore. This community is located in an area called Vijayanagar in Bangalore city. Vijayanagar is mainly a residential area with some pockets of mixed use (See Fig. 2). Based on the stakeholder feedback, the suitable alternatives for the creation of microgrids and demand response mechanisms are identified. The choice of DERs for the microgrid implementation is on the basis of local resource availability. For the study community, Vijayanagar, Bangalore, the DERs considered for creating microgrids are mainly discrete DERs such

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as LPG based CHP systems that can run as base and intermediate generating systems. The capacity of the discrete DERs selected, depend on the end-use load of the community. Biomass based CHP systems are not considered, as this technology has not reached maturity in an urban setup. Solar thermal and PV (Photovoltaic) systems are the continuous DER (distributed energy resource) technology considered. This is because the city of Bangalore is in south India and enjoys a tropical climate with adequate solar availability. Wind and hydro-based systems are not considered, as these resources are not available in Vijayanagar.

Table 2 Scenarios for community level demandesupply matching. Scenario category

Scenario Key actions

B e business as usual D e demand side management D e demand side management D e demand side management

B0 e B4 D1 D2 D12

S e supply side management

S1 e S7

S e supply side management

S8

S e supply side management

S9

DS e microgrids with DSM

DS1

DS e microgrids with DSM

DS2

DS e microgrids with DSM

DS3

3.3. .3Scenario development The categories of scenarios considered for the study based on inputs from stakeholders are given in Table 1. In all four types of scenarios e B, D, S and DS are considered. The first set of scenarios, are the Business as usual scenarios (B). The second set of scenarios, are local demand side interventions (D), which include active demand response by the community. The third set of scenarios, are on the local supply side (S) of the system. They consider installation and operation of community microgrids as a solution to localized demandesupply matching. The final set of scenarios, include both local demand and supply side interventions (DS). The key actors in the D type scenarios are consumers, utility and policy makers. The actors in S type scenarios include energy service providers who are responsible for implementing and operating the microgrids along with the consumers, utility and policy makers. The breakup of the scenarios in the different categories is presented in Table 2. Five scenarios (B0 to B4) are considered in the business as usual (B) category. These include different values of grid availability with no external intervention with a consideration of a cost for non-supply of electricity. The demand side interventions (D) include three scenarios. D1 considers demand response exercised by the consumers within the community. D2 considers the possibility of proactive scheduling some percentage of the community end-use loads. Scenario D12 considers a combination of both demand response and load scheduling. Nine scenarios are considered for the supply side interventions (S). The first seven scenarios S1 to S7 consider different values of grid availability from 24 h to 0 h. S8 considers zero grid availability with a reduction in DER technology prices. S9 considers zero grid availability with a reduction in DER technology prices and the possibility of grid interactivity (that is, selling back power to the utility). Three scenarios are considered in the demandesupply interventions (DS). DS1 is the installation of microgrids when the grid is available 24/7 along with some demand response by the community. DS2 considers installation and operation of microgrids when the grid is unavailable along with some demand response exercised by the

Business as usual Demand response Load scheduling Demand response and load scheduling Installation and operation of community microgrids for different levels of grid availability Installation and operation of community microgrids for zero grid availability and varying technology prices Installation and operation of community microgrids for zero grid availability, varying technology prices, sale of power back to the utility Installation and operation of community microgrids with 24/7 grid availability and demand response by the community Installation and operation of community microgrids for zero grid availability and demand response Installation and operation of community microgrids for zero grid availability, demand response, allowing for sale of power back to the utility

consumers. DS3 considers demand response by consumers, implementation of microgrids and possibility of grid interactivity. In order to compare the different scenarios, a MILP (multiobjective Mixed Integer Linear Program) model is used. It has been established that multi-objective MILP (Mixed Integer Linear Program) models are suitable for evaluating and designing energy solutions given their complex nature [25e30]. The optimization model, Best Fit DES [18,31e33] is used to analyze the different scenarios and to compare the results. Some of the parameters considered for comparison of scenarios are technology/resource availability, technology costs, grid availability, storage availability, efficiencies, end-use load in the community and emissions. The scenarios thus obtained are evaluated for their policy implications. Potential risks and mitigation mechanisms are identified and proposed based on stakeholder inputs. 3.3.1. Best fit DES Best fit DES is an optimization model developed to identify the ‘best-fit’ distributed energy system (microgrid) for the community

Table 1 Scenario categories for community level demandesupply matching. Issue/question

Scenario Category

Key actions

Key actors

Demandesupply gap at community level

B e business as usual

e

Demandesupply matching at community level e demand perspective

D e schedulable load (demand side)

Business as usual (status quo maintained) Demand response

Demandesupply matching at community level e supply perspective

S e microgrids (supply side)

Installation and operation of community microgrids

Demandesupply matching at community level e supply and demand perspective

DS e microgrids with schedulable loads

Installation and operation of community microgrids and demand response

U U U U U U U U U U U

Consumers Utility Policy makers Energy service providers Community (consumers) Utility Policy makers Energy service providers Community (consumers) Utility Policy makers

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that optimally matches the community energy demand (hourly loads) with available forms of supply and provides a schedule for the operation of these various supply options to maximize stakeholder utility [18,31]. It is a Mixed Integer Linear Program based tool that aids decision makers in identifying the optimal DER (distributed energy resource) mix, capacity and annual operational schedule that “best fits” the given end-use demand profile of consumers in a community and under the constraints of that community such that it meets the needs of the stakeholders. Best fit DES is a modification of DER-CAMÔ (Distributed Energy Resources Customer Adoption Model), which is developed by the Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory using the GAMS (General Algebraic Modeling System) platform [18,31e33]. Shown in Fig. 3 is the schematic of the Best Fit DES Model. 4. Results 4.1. Business-as-usual scenario The business as usual scenario is obtained from the base case of Best Fit DES. Since no explicit cost is attached to non-availability of electricity, the Electricity Utility and Government continue to follow their policy of increasing the centralized supply in the long run and resorting to load shedding in the short term. The level of load shedding is dependent on resource availability (centrally) and grid conditions. It varies in the city from 0 h during most days to about 6 h during summer months. It is assumed that the community continues to depend on the grid for meeting its electricity needs in this scenario. Any shortage in power and load shedding is borne with some minimal adjustments by the consumers. These adjustments are as follows: 1. Foregoing or postponement of some end uses. 2. Use of simple low cost alternatives such as candles and emergency lamps to deal with immediate shortages. 3. Use of expensive inverters and generators for long duration and frequent power cuts. 4. Simple lifestyle changes that result in some load shifting and load reduction. The cost to the community for different values of cost of nonsupply (Value of Lost Load) [18] is listed in Table 3. The objective function represents the total annual energy cost when the cost of non-supply (CoNS or VoLL) corresponds to zero. Two costs of non-

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supply are considered for the purpose of illustration. They are US $ 0.1 and US $ 0.2 per kWh of load that is not met. The total energy costs for the community when all of its demand (load) is met by grid-based power is US$ 238 K. If a load shedding of 1 h is considered for every day of the year, the total annual energy costs reduces from US $ 238 K to US $ 226 K if no CoNS is attached to the system. If a CoNS of US $ 0.1/kWh is attached to the load not supplied, there is a 10% increase to the total annual energy cost. A fixed CoNS of US $ 0.1/kWh results in a 65.96% increase of total cost from US $ 238 K (B0) to US $ 396 K (B4) when there is 8 h of load shedding. If the CoNS is set to US $ 0.2/kWh this value increases to US $ 418 K, which is a 75.5% increase. This implies that the community has to bear this additional cost of US$ 180 K due to the inability of the Utility to supply the total demand of the community. Presently, in India, Utilities do not consider any cost for nonsupply or value for lost load. This skews the economics of power distribution. Since any energy or demand shortages have to be met from an open source, the cost to supply power to all the consumers 24/7 without any interruption would require utilities to purchase power via open access. This cost is one of the highest and is in the range of $ 0.2 per kWh. The cost to supply a consumer in this case would be about $ 0.3 per kWh. Thus the short-term cost of nonsupply, if estimated at the supply side as the opportunity cost of alternate power, would be about $ 0.3 per kWh. It can be seen from the above results that business-as-usual scenario becomes economically unviable for longer durations of load shedding. Thus, the business-as-usual is not an effective mechanism to curb the growing demandesupply gap, given the ever-increasing demand and minimal changes made by the consumers. The risk associated with following the business-as-usual scenarios would be an exacerbation of the demandesupply gap in the future. The result would also be lower power quality and reliability, higher equipment loss and lower consumer satisfaction. 4.2. Demand side scenarios Three demand side scenarios are considered. The first scenario D1 is a proactive demand response strategy by the consumers in the community. The second scenario D2 is load scheduling. The third scenario D12 is a combination of demand response strategy and load scheduling. In the first scenario D1, the consumers have the option of foregoing, reducing or shifting their load (as shown in Table 4). The type of demand response can vary from low to medium to high. In

Fig. 3. Inputeprocesseoutput representation of best fit DES.

Please cite this article in press as: Ravindra K, Iyer PP, Decentralized demandesupply matching using community microgrids and consumer demand response: A scenario analysis, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.02.043

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Table 3 Business-as-usual scenario results. Scenario

B0 B1 B2 B3 B4

Grid availability

Objective function

Hours of availability in a day

(CoNS ¼ US $ 0/kWh)

24 23 22 20 16

Load not supplied by the grid

In US $ ‘000s 238 226 218 196 122

Total cost CoNS ¼ US $ 0.1/kWh

CoNS ¼ US $ 0.2/kWh

0.00% 10.12% 15.95% 57.77% 65.96%

238 266 282 392 418

In US $ ‘000s 0 30,326.58 48,767.59 110,550.84 168,626.07

the case of a low demand response, the consumers reduce their demand over certain hours of the day. The cost of reduction is assumed to be zero. The maximum contribution this response can make to the total load is about 30%. The maximum number of hours in a year that they can reduce their demand is limited to about 4380. In the mid demand response type, the variable cost of reducing demand is set at US $ 0.08. The maximum contribution this response can make to the total load is 10%. This option can be exercised throughout the year. In the case of a high demand response, the consumers can reduce their net load for up to 760 h in the year. The variable cost of reducing demand is about US $ 1 and up to 5% of the total load can be reduced using the high demand response. In scenario D2, a part of the consumers load is set apart as schedulable. This means that some end-uses can be postponed to different times in the day. The load is such that it can occur at any part of the day, as long as it is met at some point during that day. There is no reduction of load but merely load shifting. The amount of load that can be scheduled is set as a percentage in the Best Fit DES model. For illustration, it is assumed that 20% of the total load during the day can be scheduled. Also the maximum load in any given hour is limited to about 800 kW within the community to ensure that no peaking occurs. Table 5 presents the results obtained from the Best Fit DES model for the above data. When demand response is exercised, the total load is reduced by about 20% and the cost savings obtained is about 21.97%. When load scheduling is exercised, the total savings is about 7.74%. When both demand response and load scheduling are exercised, 25.29% savings can be obtained in the total costs. The advantage of adopting this scenario pathway is that consumers are encouraged to adopt better power management. The Utilities would be required to invest on smart meters to ensure effective implementation. Since the onus of demand management lies on the consumers, they have to be educated and empowered. However, since the scenarios only focus on the demand side, the demandesupply gap is reduced in the short term. Also this scenario does not necessarily ensure better power quality and reliability. 4.3. Supply side scenarios From the supply side, the scenarios were considered under the constraint of grid availability. Table 6 provides the results obtained from the Best Fit DES for different levels of grid non-supply. It can be seen from the results that there is an additional cost incurred for

Variable cost (US $/kW)

Maximum contribution

Maximum hours in a year

Low Mid High

0 0.08 1

0.3 0.1 0.05

4380 8760 760

Percentage increase in cost

In US $ ‘000s

238 262 276 376 396

0.00% 11.39% 17.99% 64.85% 75.55%

the investment of alternate supply options with decreasing grid availability at the community level. The percentage change in the total costs to the community, over and above the base case cost, increases from 3.65% in scenario S1 to about 33% in the scenario S7. The result of the supply side intervention is that the demande supply gap in the community reduces to zero and the reliability of energy supply increases. In scenario S7, the community can even be completely independent of the grid and meet all its energy needs locally. The additional cost required for this intervention is just 33% higher than the base case costs, which is lower than the cost of nonsupply seen in Section 4.1. There is also an improvement in the overall efficiency of the energy system. In the scenarios S1 e S7, for the Vijayanagar community, none of the continuous DERs such as solar PV and solar thermal are chosen due to their present high costs. A technology price sensitivity analysis is performed in S8. Here the price of technology is reduced by a factor of 10. The total cost in scenario 8 is less than S7 even for no grid availability. The additional cost required is just about 2% from the base case cost. S9 represents a grid interactive scenario, which incorporates the reduced technology prices and the option of selling back to the grid. In this scenario, there is a reduction in the overall objective costs and the community has the potential to earn back over 288% of its investment in the microgrid by selling the power back to the grid. Supply side scenarios are pathways that ensure local demande supply matching. They can meet the demand of the community in a reliable manner with a potential of earning back in case of grid interactivity. Presently there are no energy service providers who can adequately provide supply services at a community level. No tested business models exist for the successful implementation of community microgrids. There is a lack of codes and standards for DER technologies in India.

4.4. Demand and supply side scenarios The next set of scenarios that are analyzed are a combination of demand response and investment in DER technologies. The DS1 scenario looks at the installation and operation of community microgrids with 24/7 grid availability and exercise of Demand Response by the community. The DS2 scenario looks at the installation and operation of community microgrids for zero grid

Table 5 Best-fit DES model results for demand side scenarios.

Table 4 Demand response parameter for demand side scenario. Demand response type

Total cost

Percentage increase in cost

Base Case Scenario D1 Scenario D2 Scenario D12

Grid Total availability costs in US $ ‘000s

Overall Percentage Load Load supplied supplied efficiency cost savings by grid by DERs in GWh

24 24 24 24

1.29 1.64 1.37

238 186 220 178

0 0 0 0

0.53 0.65 0.49 0.57

0.00% 21.97% 7.74% 25.29%

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Table 6 Results of the supply side scenarios. Grid availability

Base case Scenario S0 Scenario S1 Scenario S2 Scenario S3 Scenario S4 Scenario S5 Scenario S6 Scenario S7 Scenario S8

24 24 23 22 20 18 14 12 0 0

Scenario S9

0

Total costs in US $ ‘000s

Load supplied by DERs

In US$ ‘000s

In GWh

238.18 237.46 246 254 266 278 276 296 318 244 448

Overall efficiency

0 0.25 0.32 0.41 0.83 0.90 1.02 1.11 1.32 1.51

0.53 0.53 0.55 0.55 0.56 0.56 0.56 0.56 0.52 0.95

9.49

0.7

availability and exercise of Demand Response by the community. The DS3 scenario considers the installation and operation of community microgrids with a grid interactive option for zero grid availability and exercise of Demand Response by the community. DS3 allows for sale of power back to the utility, which implies that the community is a positive net energy community, producing not only enough energy to meet its internal demand but also additional energy to supply back to the grid. Table 7 provides the results obtained from the analysis. Allowing for implementation of microgrids along with demand response, even with 24/7 grid availability results in a savings to the total costs. Significant savings of 25.06% can be obtained by resorting to a combination of demand and supply mechanisms at community level. Scenarios DS2 and DS3 present the results in the case of 24 h nonsupply of electricity from the grid. Here too it can be seen that there is a reduction in the total costs as compared to Scenario S7 of the supply side mechanism. The total costs are just 8.49% and 7.1% above the base case. This is significantly less than the 33.7% for the S7 case in Section 4.3. Demandesupply scenarios ensure local demandesupply matching along with enabling consumers to perform better power management. They can meet the demand of the community in a reliable manner with a potential of earning back in case of grid interactivity. As in the case of supply side scenarios, there are no energy service providers who can adequately provide supply services at a community level. Also utilities would have to invest in smart metering technology to ensure successful implementation. 4.5. Comparison of the scenarios Comparing the four categories of scenarios, it can be seen that sticking to the business-as-usual scenarios may result in exacerbation of the demandesupply gap. Demand side interventions result in savings in the total costs for the community. However, they do not aid communities with load shedding. Supply side interventions are expensive. But they increase the reliability of the

Percentage change 0.00% 0.006% 3.65% 6.67% 11.43% 16.83% 15.59% 23.98% 33.14% 2.07% 288.17%

DERs chosen (kW) Discrete

Continuous

Storage batteries

0 100 100 100 200 200 200 200 200 200

0 0 0 0 0 0 0 0 0 Solar Solar Solar Solar

0 0 656.16 894.58 312.14 515.87 346.23 431.11 314.05 387.85

200

PV e 442.75 kW thermal e 416.5 kW PV e 4581.96 kW thermal e 24.71 kW

319.27

energy system for a small additional cost. Communities even have the opportunity to meet their energy needs independent of the grid. The combination of both demand and supply side interventions is the best solution alternative for communities. Choosing both interventions enables communities to meet their energy needs 24/7 in a reliable manner and also do it at a lower cost. With microgrid implementation, communities have the added opportunity to sell back power to the grid for a profit. For the Vijayanagar community in Bangalore, India, a microgrid system built using Discrete DERs (LPG based CHP systems along with storage batteries) along with consumer demand response proves to be the best augment to centralized grid power to solve the demandesupply gap at the community level. This solution may be different for other communities depending on the specific community, its demand patterns, technology/resource availability, technology costs, grid availability, storage availability, efficiencies, end-use load in the community and emissions. 5. Risks and mitigation As part of the stakeholder workshop, one of the objectives was to identify and enumerate the barriers that hinder implementation of microgrids in communities. The stakeholders then proposed minimization strategies that would help reduce the impact of these barriers and enable implementation of microgrids. Table 8 presents the barriers to microgrid implementation and their minimization strategies based on literature [34e37] and inputs from the stakeholders. The barriers that hinder the implementation of community microgrids are operational barriers, financial barriers, social barriers, technical barriers, stakeholder issues and policy barriers. The operational barriers include the lack of standards and options, the issue of acceptability by the industry and the public and the issues related to the maintenance and upkeep of the microgrid systems. Microgrids are nascent technologies that have not been implemented in developing countries. There is a need for the development of codes, standards and labeling programs for the

Table 7 Results of the demand and supply side scenarios.

Base Case Scenario DS1 Scenario DS2 Scenario DS3

Grid availability

Total costs in US $ ‘000s

24 24 0 0

238.18 178.5 258.38 255.08

Load supplied by DERs In GWh 0 0.2565 1.1477 1.2148

Overall efficiency

Percentage change

0.53 0.59 0.63 0.6

0.00% 25.06% 8.49% 7.10%

DERs chosen (kW) Discrete

Continuous

Storage batteries

0 100 200 200

0 0 0 0

0 0 126.93 99.11

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K. Ravindra, P.P. Iyer / Energy xxx (2014) 1e10

Subsidies and financial incentives Education and empowerment of consumers Specifically targeted programs Capacity building initiatives R&D initiatives

developing countries. Test beds and showcase implementations of microgrids for different energy requirements would enable quicker learning and shorter diffusion periods. Stakeholders play a very important role in the successful implementation of microgrids. Currently stakeholders are plagued by a lack of harmony and trust. There is a need for commitment and coordinated effort that will increase stakeholder interactions and participation. This will in turn enable better decision-making. Some of the policy barriers to the implementation of microgrids are the lack of suitable policies, lack of accountability and high political interference. Political decoupling, increasing transparency and ensuring accountability by introducing autonomous regulatory authorities are possible strategies to deal with these barriers. Specific policies need to be passed to operationalize the process of implementing community level microgrids.

Increase stakeholder interactions and participation Transparency measures

6. Conclusion

Table 8 Barriers to implementation of community microgrids and their mitigation strategies. Barriers

Nature

Mitigation strategy

Operational

Lack of standards and options Acceptability by the industry and public Maintenance and upkeep of systems Affordability (capacity to pay) Capital cost Lack of education Cultural dimensions Rich-poor divide Lack of interest Technology availability Technology maturity Commitment Coordinated effort Lack of harmony and trust Accountability Political interference Lack of policy

Development of codes and standards, labeling programs Education and empowerment of consumers Capacity building initiatives

Financial

Social

Technological Stakeholders

Policy

Micro finance options

Political decoupling Specific policy

different microgrid technologies and appliances. Consumers are unaware of the technologies that exist and their value. They have to be educated and empowered about the need for the implementation of local energy systems and for changing their demand patterns. Capacity building initiatives should be undertaken to enable proper maintenance and upkeep of the microgrid technologies. The financial barriers are the issues of affordability and high capital costs of microgrid technologies. Consumers may not have the capacity to pay for new technologies, as they have not reached economies of scale. The capital cost for renewable DERs specifically solar PV and thermal is presently very high. Subsidies and financial incentives have to be offered to ensure the purchase of microgrids by communities. The social barriers include lack of education of consumers, cultural dimensions, the increasing rich-poor divide and lack of interest among stakeholders. The lifestyles of consumers affect their choice of energy options. Any new intervention is looked at with suspicion and takes time to be incorporated into their daily routine. Also the growing rich and poor divide may further increase when communities, which have the means, resort to the purchase of alternatives. Then the government or utility may not have sufficient cause to ensure better access. Poorer communities may get neglected and lag further behind. The mitigation strategies for these issues are the education and empowerment of consumers combined with specifically targeted programs for different income groups and communities. Capacity building initiatives can help increase interest among stakeholders. These initiatives ensure proper implementation of solutions tailored to the needs of different communities. Some of the technical barriers include availability of suitable technology and their maturity. Microgrid and DER technologies are new and still in the innovation and early growth stage [38e40]. Thus costs of these technologies have not reached their maturity levels. These technologies are being developed for the industrialized countries and may not be suitable as is for developing countries. Focused R&D initiatives need to be initiated by both public and private institutions to develop technologies suitable for

Scenario analysis compares potential decision alternatives on the basis of parameters such as the technology/resource availability, technology costs, end-use load in the community and stakeholders objectives. Four categories of scenarios were developed based on potential interventions. These were business-as-usual, demand side, supply side and demandesupply side. About 21 scenarios were identified and administered to a community in Vijayanagar, Bangalore, India and their results compared. Comparing the four categories of scenarios, it is seen that business-as-usual scenarios may result in exacerbation of the demandesupply gap. Demand side interventions result in savings in the total costs for the Vijayanagar community, but cannot aid it with the load shedding. Supply side interventions increase the reliability of the energy system for a small additional cost and the community has the opportunity to meet its energy needs independent of the grid. The combination of both demand and supply side interventions are the best solution alternative for community as they enable the community to not only meet its energy needs 24/7 in a reliable manner but also do it at a lower cost. With the microgrid implementation, the Vijayanagar community has the added opportunity to sell back power to the grid for a profit. Finally a list of potential risks and minimization strategies were enumerated based on inputs from experts and stakeholders. In all, the scenario analysis framework enables social, economic and policy level discussions to choose the best microgrid solution. It enables a dialog process with the stakeholders. It is important to note that even though solutions are available and are the best, there are several barriers that may hinder the implementation of microgrids. These barriers need focused minimization strategies to deal with.

Acknowledgments The authors are grateful to Dr. Chris Marnay and Dr. Michael Stadler of the Environmental Energy Technologies Division, Lawrence Berkeley National Labs, California, for enabling us to use the DER-CAMÔ (Distributed Energy Resources Customer Adoption Model) and modifying it to suit the developing country context.

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Appendix A. Nomenclature

Base load Combined heat and power (CHP) generation CoNS Demand response (DR) Demand side management (DSM)

DER/DES DER-CAMÔ

GAMS Gigawatt-hour (GWh) Kilowatt (kW) Kilowatt-hour (kWh) Load Load shedding LPG MCDM MILP Non-supply of electricity Solar photovoltaic (PV) VoLL

The average minimum demand on an electricity system, usually measured over a 24-hour cycle. Simultaneous production of both heat and power. The difference with co-generation is that the generation of heat and power may be done as parallel processes, which results in a lower overall efficiency. Cost of non-supply In electricity grids, demand response (DR) is similar to dynamic demand mechanisms to manage customer consumption of electricity in response to supply conditions. The planning, implementation and monitoring of activities designed to encourage consumers to modify their pattern of energy use. The process of managing the consumption of electrical energy, generally to minimize demand and costs. Distributed Energy Resources or Distribute Energy Systems The Distributed Energy Resources Customer Adoption Model (DER-CAM) is an economic and environmental model of customer DER adoption. The MILP optimization model chooses which DG and/or CHP technologies a customer should adopt and how that technology should be operated based on specific site load and price information, and performance data for available equipment options. General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming and optimization Gigawatt-hour (GWh) is a unit of energy. 1 GWh is equal to 1 million kilowatt-hours. A unit of electric power or capacity equal to 1000 W. Kilowatt-hour (kWh) is the basic measure of electric energy generation or use. 1 kWh is the supply of 1 kW for a period of 1 h. If an electric circuit has a well-defined output terminal, the circuit connected to this terminal (or its input impedance) is the load. The term ‘load’ may also refer to the power consumed by a circuit. Utilities may impose load shedding on service areas via rolling blackouts or by agreements with specific high-use industrial consumers to turn off equipment at times of system-wide peak demand. Liquefied petroleum gas Multi-criteria decision methods Mixed Integer Linear Programming, an optimization technique Electricity that is not supplied to the consumers when demanded using various non-supply or rationing options. Technologies that convert sunshine into electricity using solar cells (photosensitive silicon cells). Value of lost load. The Value of Lost Load (VoLL) is the estimated amount that customers receiving electricity with firm contracts would be willing to pay to avoid a disruption in their electricity service.

B. Optimization model e DER-CAM The Distributed Energy Resources Customer Adoption Model (DER-CAM) is an economic and environmental model of customer DER adoption. This model has been in development at the Lawrence Berkeley National Lab since 2000. The objective of the model is to minimize the cost of operating on-site generation and combined heat and power (CHP) systems, either for individual customer sites or a mGrid. In other words, the focus of this work is primarily economic. To achieve this objective, the following issues must be addressed:  Which is the lowest-cost combination of distributed generation technologies that a specific customer can install?  What is the appropriate level of installed capacity of these technologies that minimizes cost?  How should the installed capacity be operated so as to minimize the total customer energy bill? More information can be obtained on the website http://der.lbl. gov/der-cam and http://www.cet.or.at/pdf_files/FlyerDERCAM3. pdf. References [1] Strupeit L, Peck P. Introduction to scenario development, developing emission scenarios to aid air pollution prevention and control e a guideline manual for RAPIDC in South Asia. SEI; 2008. [2] Rotmans J. Methods for IA: the challenges and opportunities ahead. Environ Monit Assess 1998;3:155e79. [3] Rotmans J, van Asselt MBA, de Bruin AJ, den Elzen MGJ, de Greef J, Hilderink H, et al. Global Change and Sustainable Development: a modelling perspective for the next decade. Bilthoven, The Netherlands: National Institute of Public Health and Environmental Protection (RIVM); 1994. [4] Ringland G. Scenario planning: managing for the future. New York: John Wiley; 1998.

[5] Ringland G. Scenarios in public policy. Chichester, England: John Wiley & Sons, Ltd; 2002. [6] Ghanadan Rebecca, Koomey Jonathan G. Using energy scenarios to explore alternative energy pathways in California. Energy Policy June 2005;33(9): 1117e42. [7] Gallopin GC, Raskin P. Windows on the future: global scenarios & sustainability. Environ Sci Policy Sustain Dev 1998;40(3):6e11. [8] Strupeit L, Peck P. Developing emission scenarios to aid air pollution prevention and control e a guideline manual for RAPIDC in South Asia. International Institute for Industrial Environmental Economics (IIIEE), Lund University; 2008. [9] Harris GS, Ellis MJ, Scott GC, Wood JR, Phillips PH. Energy scenarios for New Zealand. Energy February 1978;3(1):1e14. [10] Topcu YI, Ulengin F. Energy for the future: an integrated decision aid for the case of Turkey. Energy January 2004;29(1):137e54. [11] Østergaard Poul Alberg, Mathiesen Brian Vad, Möller Bernd, Lund Henrik. A renewable energy scenario for Aalborg Municipality based on lowtemperature geothermal heat, wind power and biomass. Energy December 2010;35(12):4892e901. [12] Pillai Jayakrishnan R, Heussen Kai, Østergaard Poul Alberg. Comparative analysis of hourly and dynamic power balancing models for validating future energy scenarios. Energy May 2011;36(5):3233e43. [13] Zhang Qi, Ishihara Keiichi N, Mclellan Benjamin C, Tezuka Tetsuo. Scenario analysis on future electricity supply and demand in Japan. Energy February 2012;38(1):376e85. [14] Hong Sanghyun, Bradshaw Corey JA, Brook Barry W. Evaluating options for sustainable energy mixes in South Korea using scenario analysis. Energy 1 April 2013;52:237e44. [15] EP. Sixth Community Environment Action Programme. Brussels: European Parliament and the Council of the European Union; 2002. [16] Schwartz P. The art of the long view: planning for the future in an uncertain world. New York: Currency Doubleday; 1991. [17] Schwartz P. Using scenarios to navigate the future. Internal document. Global Business Network; 1999. [18] Ravindra K, Iyer P. A conceptual model for creation of local grids (distributed generation systems) based on renewable energy sources in urban regions of developing countries: a case example of Bangalore. In: Renewable Energy Asia e 2008, an International Conference. IIT Delhi: Narosa Publishing House Pvt Ltd; 11e13 December, 2008. [19] Ravindra K. Community microgrids for decentralized energy demand e supply matching: an integrated decision framework. PhD Thesis. Bangalore: Management Studies, Indian Institute of Science; 2012. [20] Markovska Natasa, Dui c Neven, Guzovi c Zvonimir, Mathiesen Brian Vad, Lund Henrik. Our common future e 25 years later: sustainable development

Please cite this article in press as: Ravindra K, Iyer PP, Decentralized demandesupply matching using community microgrids and consumer demand response: A scenario analysis, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.02.043

10

[21]

[22] [23]

[24] [25]

[26] [27]

[28]

[29]

[30]

K. Ravindra, P.P. Iyer / Energy xxx (2014) 1e10 WHATs, HOWs and WHOs of energy, water and environment systems. Energy 1 August 2013;57:1e3. Shen Yung-Chi, Chou Chiyang James, Lin Grace TR. The portfolio of renewable energy sources for achieving the three E policy goals. Energy May 2011;36(5): 2589e98. Balachandra P, Chandru Vijay. Modelling electricity demand with representative load curves. Energy March 1999;24(3):219e30. Maddigan Ruth J, Rizy Colleen Gallagher. Modeling demand and supply interactions to forecast load growth for electricity distribution systems. Energy February 1984;9(2):149e62. Sudhakara Reddy B, Srinivas T. Energy use in Indian household sector e an actor-oriented approach. Energy August 2009;34(8):992e1002. Pérez-Fortes Mar, Laínez-Aguirre José Miguel, Arranz-Piera Pol, Velo Enrique, Puigjaner Luis. Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy August 2012;44(1):79e95. Bayod-Rújula Angel A. Future development of the electricity systems with distributed generation. Energy March 2009;34(3):377e83. Fazlollahi Samira, Mandel Pierre, Becker Gwenaelle, Maréchal Francois. Methods for multi-objective investment and operating optimization of complex energy systems. Energy September 2012;45(1):12e22. lu Gürkan, Or Ilhan. Modeling and analysis of a Sarıca Kemal, Kumbarog decentralized electricity market: an integrated simulation/optimization approach. Energy August 2012;44(1):830e52. Browne David, O’Regan Bernadette, Moles Richard. Use of multi-criteria decision analysis to explore alternative domestic energy and electricity policy scenarios in an Irish city-region. Energy February 2010;35(2):518e28. Ribeiro Fernando, Ferreira Paula, Araújo Madalena. Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: the Portuguese case. Energy 1 April 2013;52.

[31] Siddiqui Afzal S, Marnay Chris. Distributed generation investment by a microgrid under uncertainty. Energy December 2008;33(12):1729e37. [32] Marnay C, Chard JS, Hamachi KS, Lipman T, Moezzi MM, Ouaglal B, et al. Modeling of customer adoption of distributed energy resources. LBNL-49582; August 2001. [33] Stadler M, Siddiqui A, Marnay C, Aki H, Lai J. Optimal technology investment and operation in zero-net-Energy buildings with demand response. Presented at Advanced Workshop in Regulation and Competition, Monterey, California, USA. LBNL-1973E; June 2009. [34] Kaundinya Deepak Paramashivan, Balachandra P, Ravindranath NH, Ashok Veilumuthu. A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: a case study of Tumkur district, India. Energy 1 April 2013;52:77e88. [35] Pillai Indu R, Banerjee Rangan. Renewable energy in India: status and potential. Energy August 2009;34(8):970e80. [36] Parikh Kirit S, Karandikar Vivek, Rana Ashish, Dani Prasanna. Projecting India’s energy requirements for policy formulation. Energy August 2009;34(8):928e41. [37] Nixon JD, Dey PK, Davies PA. The feasibility of hybrid solar-biomass power plants in India. Energy October 2012;46(1):541e54. [38] Lo Prete Chiara, Hobbs Benjamin F, Norman Catherine S, Cano-Andrade Sergio, Fuentes Alejandro, von Spakovsky Michael R, et al. Sustainability and reliability assessment of microgrids in a regional electricity market. Energy May 2012;41(1):192e202. [39] Quiggin Daniel, Cornell Sarah, Tierney Michael, Buswell Richard. A simulation and optimisation study: towards a decentralised microgrid, using real world fluctuation data. Energy May 2012;41(1):549e59. [40] Pina André, Silva Carlos, Ferrão Paulo. The impact of demand side management strategies in the penetration of renewable electricity. Energy May 2012;41(1):128e37.

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