A review of demand-side management: Reconsidering theoretical framework

Renewable and Sustainable Energy Reviews 80 (2017) 367–379

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

A review of demand-side management: Reconsidering theoretical framework A. Fattahi Meyabadi, M.H. Deihimi

MARK

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Department of Electrical Engineering, Hamedan University of Technology, Hamedan 65155-579, Iran

A R T I C L E I N F O

A BS T RAC T

Keywords: Demand response Demand-side management Electrical load management Energy conservation Orderly power utilization

Demand-side management (DSM) has a crucial role in the attainment of sustainable energy that aims to optimize the energy utilization and mitigate emissions. Hence, DSM enhances the flexibility in the power system operation and facilitates low carbon transition in the electricity generation. Recently, the conventional terminology and strategies of DSM have been reformed to match with deregulation of energy environment. Furthermore, the increasing penetration of distributed energy resources (DERs) as well as the advent of smart grids have diversified the techniques and methods of DSM. This paper aggregates the expressions and methodologies about DSM subjects used by the literature and presents explicit definitions of the relevant concepts. In contrast with other review papers in DSM arena, this paper proposes a novel theoretical framework which aims to unify the terminology, concepts, and modalities associated with the literature. A comprehensive categorization of DSM strategies is presented, the scopes are clarified, and the relevant modalities are explicated to attain the unequivocalness in terminology. The evolution of DSM as well as state of the art concepts are considered in the organization of this paper. Moreover, the methods of DSM are reviewed under the paradigms applied in the accomplished classification.

1. Introduction There are two dissimilar strategies in the power system operation to encounter total predicted load demand: expanding the electricity generation which provides new physical supply-side energy resources, and employing the managerial measures which provides virtual demand-side resources. The first strategy is pursued to provide additional energy quantity to meet the increasing load demand emerged due to modernization or economic development. The second strategy declines the augmentation of energy supply in response to the growth in the load demand and aims to uphold the parsimony in the energy sector through the implementing of appropriate managerial measures and the postponing of generation capacity augmentation. The concept of demand-side management (DSM) has been emerged from the implementing of the managerial measures to produce the resources on the demand-side by influencing the load demand. The term ‘demand-side management’ was coined by Clark W. Gellings in the 1980's. The basic theoretical frame of DSM has been founded on the concepts addressed in [1–5]. Thus, DSM was encompassed the scheming, implementation, and monitoring of the utility contrivances and programs that can influence the electricity utilization by changing the consumption pattern of the customers to attain the desired changes

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Corresponding author. E-mail addresses: [email protected], [email protected] (M.H. Deihimi).

http://dx.doi.org/10.1016/j.rser.2017.05.207 Received 6 January 2016; Received in revised form 23 March 2017; Accepted 23 May 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

in the load shape [1]. Ref. [1] has itemized the schemes of DSM including electrical load management, strategic conservation, building loads, and power marketing. The clients and utilities may independently treat to alter the consumption pattern but the concept of DSM entails a utility/client relationship that makes mutually advantageous outcomes [2]. The scope of DSM has been restricted to the scheming and implementation of programs to vigorously shape the electrical load profiles to bring about better energy utilization, lower operation costs and financial stability in [3]. A helpful critical review of DSM implementation on the basis of the concepts embedded in the basic theoretical frame of DSM has been presented in [6]. Since the 1990's, in order to harmonize the basic theoretical frame of DSM with power system restructuring, many concepts, techniques, and terms have been introduced in the literature. Nonetheless, the framework of DSM was retained in the 1990's. For instance [7], and [8] have reviewed the impacts and utility experiences with DSM bidding programs. Moreover, the concept of demand-side response (that had been early introduced by [9]) has been widely employed in the literature (e.g. in [10–13]). Thereafter, price-responsive DSM has become an important subject of DSM. Ref. [14] has comprehensively discussed about the concepts of price-responsive DSM. The fundamentals of DR are clarified in [15]. Moreover, the penetration of distributed generators

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ELCS ELM ENCON FP-CPP IRP LF LFC LPC MCP OPU RTP SDSM SELM SENCON TDLC TOU V-PP VP-CPP VSS

Nomenclature ADSRCC Available Demand-Side Resource Capacity Control ADSRM Available Demand-Side Reserve Management AMI Advanced Metering Infrastructure CHP Combined Heat and Power CILC Contractual Indirect Load Control CPP Critical Peak Pricing CPP-LC Critical Peak Pricing with load control CPR Critical Peak Rebates DDSM Dynamic Demand-Side Management DELM Dynamic Electrical Load Management DENCON Dynamic Energy Conservation DER Distributed Energy Resource DG Distributed Generator DR Demand Response DSI Demand-Side Integration DSM Demand-Side Management ECM Energy Consumption Management EEM Energy Efficiency Management

Electric Load Curve Synthesis Electrical Load Management Energy Conservation Fixed Period Critical Peak Pricing Integrated Resource Planning Load Factor Load Factor Correction Load Profile Correction Market Clearing Price Orderly Power Utilization Real-time Pricing Static Demand-Side Management Static Electrical Load Management Static Energy Conservation Technological Direct Load Control Time of use tariff Variable Peak Pricing Variable Period Critical Peak Pricing Variable Service Subscription

helpful literature review about industrial energy saving via managerial schemes, technologies and policies. The DR has been well focused from different perspectives in many surveys and review papers [34–43]. Perusal of the papers published during past three decades exhibits a significant lacuna in theoretical frame of DSM due to the employing of various expressions and methods some of which lack clear definitions. Moreover, in many cases, the main difference between some methods have not been distinguished in the literature and these different methods may be appeared to be alike. Thus, the evolution of DSM's concepts and methods as well as the lack of explicit definitions of some concepts in the literature, imply the necessity of contriving an aggregated theoretical framework for this branch of energy management. This paper presents a comprehensive review about DSM, clarifies the basic concepts, phrases, general subjects and practical methods of DSM by reconsidering the DSM theoretical framework. The conventional frame of DSM is reformed in this paper with particular standpoint to clearly describe the specific goals of known methods. The structure of the theoretical framework proposed in the present paper, may facilitate the analysis of applicable methods for implementers and participants. The main novelties and contributions of this paper are listed in the following:

(DGs), the prevalence of competitive electricity markets, the advancement of end-use technologies and control systems, and the advent of smart grids result in the reform of conventional DSM theoretical frame. Main DSM concepts has been itemized in [16] including energy efficiency, energy conservation (ENCON), and demand response (DR). These concepts are the significant facets of reformed DSM theoretical frame in the recent decade. Ref. [17] has provided a review of historical DSM developments in which the DSM has been categorized to energy efficiency, DR, and strategic load growth. As a general classification, DSM is divided into two wide-range concepts including energy efficiency and DR in [18] and [19] based on the impact of technological advances in smart grids and electricity market deregulation. Moreover, some papers have employed the concept of demandside integration (DSI) to accommodate the concept of DSM with the modern concepts emerged after the deregulation of energy environment [20–27]. According to [17], despite these terminology alterities, the terms of electrical load management originated in the traditional regulated power systems are still used in the restructured power systems. There are worthwhile papers that have focused on DSM [16,18,28– 32]. Ref. [16] has reviewed the DSM programs designed and implemented in several countries. Ref. [28] has presented a historical review of DSM on the basis of the development status of power industry and electricity markets in China with energy conservation and emissions reduction scope. Ref. [29] has regarded DSM as a demand control technique and has overviewed the DSM methods with load management and tariffing scope. According to [29], the main goal of DSM is encouragement of clients to reduce power consumption during peak periods or shift of energy use from peak to off-peak hours to flatten the load curve. Ref. [30] has presented an overview of DSM strategies and technologies for mini-grids with energy efficiency and incentives scope. Peter Warren has proposed a definition for DSM after expressing contested definitions of DSM in [31]: DSM involves the technologies, activities and schemes on the demand-side in order to manage energy consumption or contribute to the attainment of energy policies such as gas emissions mitigation or energy balancing. Ref. [31] has reviewed DSM concept and its role in balancing mechanism as well as electricity market reform in the UK. Ref. [32] has presented a helpful overview on demand-side resources development from controllable loads to generalized demand-side resources which has a comprehensive regard to DSM; however, the classification of DR discussed in [32] follows the previous works. Some review papers published in the recent years, have mostly focused on a certain aspect of DSM. Ref. [33] has presented a

1. Some expressions such as ‘static’ and ‘dynamic’ methods are introduced for the first time. 2. The ENCON is analyzed with an individual and specific way on the basis of explicitness in definitions. In comparison with other papers, this concept is exactly clarified. For instance, the differences between the concepts energy saving, energy auditing, and energy recovery is clearly elucidated in the present paper. 3. The mechanisms, modes, strategies, and methods are separated in the proposed DSM theoretical framework. 4. The energy efficiency management (EEM) and energy consumption management (ECM) are separated and the relevant strategies are clarified. 5. Orderly power utilization (OPU) is separated from DR. These two concepts are comprehensively discussed. 6. In the present paper, the DR is associated with the integration of demand-side resources in power system operation. Thus, the proposed DSM theoretical framework involves the concepts of integrated resource planning (IRP), reliability, security and electricity pricing. 368

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3.2.1. Electrical load management (ELM) 3.2.1.1. History of ELM. ELM was introduced in the seventies as the first step in applying of managerial measures. The ELM is also taken to account as the first concept of DSM theory [50–63]. Early, DSM was known as load management [31]. Also, the concept of DSM was initially restricted to the load management e.g. in [1–3,5]. A classification of ELM concepts has been done in [1] in which the term ‘demand-side management’ has been applied instead of the term ‘load management’ defined as the scheming, implementation, and monitoring of the utility contrivances and programs to influence the electricity utilization which result in desired changes in the consumption pattern of clients. Moreover, peak clipping, valley filling, and load shifting have been introduced as three classic forms of ELM and strategic load growth, strategic conservation, and flexible load shape have been introduced as three modern forms of ELM in [1]. Nowadays the definitions of concepts and methods of ELM presented in [1] are generally referred as a standard frame. The concept of peak load reduction has been addressed in [50] with two approaches. The first approach is the tariffing on peak-load hours for load shifting i.e. diverting some energy requirements to off-peak hours which may be declared by individual indicators to inform the clients and the second approach is the employment of load control devices. Both approaches need communication systems for utilities and clients. A comparison of applying transmission lines as the communication medium and employing radio network has been presented in [50].

7. This paper aggregates former definitions related to DSM theory from the literature and hence, involves all of the concepts and known methods in evolution of DSM. The rest of the paper is organized as follows. Section 2 explains the standpoint about formatting of the proposed framework, Section 3 develops and explicates DSM theoretical framework and clarifies the relevant concepts, and the main conclusions are described in Section 4. 2. Standpoint of analysis and presentation A detailed categorization of DSM history in China has been presented in [28] including two general categories: ‘traditional DSM’ and ‘DSM aimed at energy conservation and emissions reduction’. The traditional DSM has been classified to ‘traditional power load management’, ‘DSM before the electric power system reform’, and ‘DSM after the electric power system reform’. The mentioned categorization is based on evolution analysis in the studied location which can be substantially generalized for other power systems. This timescale is implicitly considered in the DSM theoretical framework proposed in the present paper. Moreover, some needful expressions are devised to precisely describe the methods. Also, a particular taxonomy of DSM is presented from power system operator's point of view. Integrating DSM into utility planning has been discussed in [3,5]. According to [5], uncertainties of load demand, energy prices, construction costs, accessibility and the costs of purchasing power from other suppliers, and the regulatory energy environment have led the utilities toward embedding DSM concepts in their energy resource planning. DSM, from the managerial viewpoint, can be defined as a set of energy policies and managerial measures on the electrical energy demand-side that makes virtual resources in order to upgrade the energy planning in the power systems. The concept of IRP emerges from such standpoint [44–49]. The DSM theoretical framework proposed in the present paper involves all the concepts of power system operation and energy management. The DSM theoretical framework proposed in this paper is organized after profound study of DSM on the basis of inspection and critique. Furthermore, it is very important that the concepts and techniques of DSM in previous works must be unchanged in the proposed theoretical framework. Hence, some prominent references are addressed in the DSM theoretical framework proposed in this paper wherever they have been required.

3.2.1.2. Concept of ELM. The first scientific definition of ELM has been presented by ‘Load Management Working Group of the System Planning Subcommittee of the Power Engineering Committee’ in [51]: The load management is the intentional influencing of clients so as to shift the time of use for electricity. Three alternatives of ELM including ‘direct load control (through technological measures implemented by utilities)’, ‘indirect load control (through various forms of electricity pricing)’, and ‘energy storage’ have been addressed in [51]. These three alternatives are also described in [52] as demand-side load management which have been compelled to the utilities by the rising production cost of peak periods and the high difficulty of capacity expansion. Ref. [53] has detailed the electric load curve synthesis (ELCS) model which can be applied to predict the changes in load shape due to both load management and non-load management changes. Ref. [54] has categorized ELM into five general groups including energy storage, interruptible loads, conservation (controlled), customer load control, and dispersed generation. It has also defined ELM options and has described some forecasting techniques which can be applied to ELM. A typical assessment methodology of load management has been presented in [55] which involves all components of incorporated strategic planning system. Moreover, an analytical framework of load management including significant considerations of the customer, electricity rates, forecasting, marketing and operations has been discussed in [55]. A glossary of terms related to ELM has been published in [61].

3. DSM theoretical framework The theoretical framework of DSM involves the concepts described as follows: 3.1. DSM strategies and modalities The main objective of DSM is the matching of the electricity supply with demand. In order to attain this objective, there are two different strategies:

3.2.1.3. Modalities and mechanisms of ELM. In the DSM theoretical framework proposed by the present paper, the forms of ELM presented in [1] are considered as mechanisms that fulfills the objectives of ELM. Similar to that was described for DSM modalities, there are two modalities of ELM addressed in the following.

1. Consumption reduction 2. Efficiency improvement Accordingly, DSM is classified to two modalities called ‘static DSM (SDSM)’ and ‘dynamic DSM (DDSM)’, respectively.

3.2.1.3.1. Static electrical load management (SELM). SELM refers to the measures and activities that aims to reduce the electricity consumption when it is required. The SELM involves ‘strategic conservation’ which aspires to improvement of consumption pattern, and ‘flexible load shape’ associated with

3.2. Methodology of DSM There are two general techniques in the structure of DSM (both SDSM and DDSM) clarified in the following: 369

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reliability scope in the power system operation. The SELM is analyzed by the following mechanisms associated with the load profile [1,17,29] in which the SDSM strategy is considered:

3.2.2.1. Strategies of ENCON. ENCON aims to reduce the energy loss in a system. There are two different strategies of ENCON described in the following.

1. Strategic Conservation: Utility-stimulated diminution of the load to modify the load shape 2. Flexible Load Shape: Reliability-based changing of the load.

3.2.2.1.1. ECM. ECM means energy conservation through terminal energy control. It is incontrovertible that the increase in the terminal energy is out of ENCON scope. 3.2.2.1.2. EEM. EEM means energy conservation through energy efficiency control. It is indisputable that any decrease in the energy efficiency is out of ENCON scope.

The SELM includes ‘strategic SELM’ and ‘reliability-based SELM’. The strategic SELM involves utility-stimulated diminution of load to surmount overall energy shortage. Thus, the mechanism of strategic SELM is ‘strategic conservation’. The reliability-based SELM aims to meet the energy needs of clients when unanticipated events lessen the amount of available power. The mechanism of reliability-based SELM is flexible load shape mostly implemented via individual consumer load control devices. 3.2.1.3.2. Dynamic electrical load management (DELM). DELM aims to improve the usefulness of power utilization. The DELM involves the measures and activities that make desirable changes in the load curve. Load Factor (LF) is the main index to describe usefulness of power utilization. The LF is defined as the ratio of average load of a certain time period to maximum load in the same time period. A great difference between power consumptions related to peak and off-peak periods results in great investment losses and noneconomic generation in the power system. DELM objectives are ‘load factor correction (LFC)’ including peak clipping, valley filling, and load shifting mechanisms (designed for increasing LF), as well as ‘load profile correction (LPC)’ with strategic load growth mechanism (designed for improving the efficiency of power system operation). The DELM is analyzed by the following mechanisms associated with the load profile [1,17,29] to attain the goals of DDSM:

3.2.2.2. Mechanisms of ENCON. The energy balance of an energy system is described by Eq. (2) where El represents total energy losses.

Et = Eu + El

We can obtain Eqs. (3) and (4) from dividing both sides of Eq. (2) to Et and Eu respectively. (3)

⎛1 ⎞ El = Eu⎜ − 1⎟ ⎝η ⎠

(4)

3.2.2.2.2. Energy audit. Energy audit involves the measures that decrease energy losses under definite and unchanged useful energy. Consequently, according to Eq. (4) the energy efficiency is improved and according to Eq. (1) terminal energy decreases. Thus, required energy and energy cost of end-user are decreased. In this mode the output is unchanged. The main aim of energy audit is the cost reduction and the mitigation of environmental pollutants. Using the insulators in an equipment or buildings is a sample of energy auditing. Energy audit is very important for industrial loads and results economical benefit as well as other social benefits. Energy audit is usually known as the inspection activities and the perusal of energy flow to find the energy conservation opportunities for energy consumption reduction without impacting the output [33]. This perspective of energy audit has been used in [75,76].

3.2.2. ENCON ENCON involves the measures applied for the end-use to influence the quality of energy consumption attitude that can be characterized by technical indexes. The main goal of ENCON is energy loss reduction in an energy system. Generally, an energy system is a system fed by energy that can provide specific service output. Usually, the term ‘energy system’ refers to an industrial process, an equipment of a process, or an appliance that consumes energy and provides some services. The schematic of an energy system is shown in Fig. 1. An energy system involves an input called ‘terminal energy’, an output called ‘useful energy’, and energy losses. The terminal energy is defined as the energy which is available to use after subtraction of distribution losses and quantities stored. Customers pay on their bills because of terminal energy utilization. The useful energy is defined as the required energy for doing work. In practice, it is used to achieve the energy services such as heating, illumination and motive force. The quantity of useful energy obtained from a certain quantity of terminal energy, depends on the efficiency of the system. The more efficiency of an energy system is, the less energy wastes in the system. The energy efficiency is defined according to Eq. (1) where Eu and Et represent the useful and the terminal energy, respectively.

Eu Et

El = Et (1 − η)

Three mechanisms of ENCON can be defined: 3.2.2.2.1. Energy recovery. Energy recovery involves the measures that decrease energy losses under definite and unchanged terminal energy. Consequently, according to Eq. (3) the energy efficiency is improved and according to Eq. (1) energy services obtained from a certain purchased energy increase because the useful energy rises. The combined heat and power (CHP) is a lucrative scheme to recover energy [64–68]. The CHP is exploited in a kind of controllable DGs [19]. From economical perspective, for a certain payment of consumed energy, further services can be attained via waste reduction. Thus, the concept of energy recovery can be generalized to energy productivity defined as the amount of possible economic output at a given level of energy input in a system. Thereby, energy productivity can be considered as an evaluation index in DSM similar to the energy efficiency. Refs. [69–74] have studied the energy productivity from such perspective.

1. Peak-clipping: System peak load reduction (at peak load time periods) 2. Valley Filling: Building off-peak loads 3. Load Shifting: Shifting the load from on-peak to off-peak time periods 4. Strategic Load Growth: Utility-stimulated increment of the load

η=

(2)

(1)

Fig. 1. Schematic of an energy system.

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‘producing educational programs so as to show the social benefits of power utilization at off-peak hours’, and ‘electrification plans’ are examples of SDSM.

3.2.2.2.3. Energy saving. Energy saving involves the measures that decrease energy losses under definite and unchanged energy efficiency. There is only one feasible way to energy losses reduction with no change in the internal parameters: terminal energy reduction. The terminal energy and the useful energy decrease according to Eqs. (3) and (4) respectively and the energy services will decrease. The less terminal energy is, the less energy services are obtained. Thus, the further energy can be saved. Correction of lighting system is a sample of energy saving. The different aspects of energy saving have been presented in [33,77].

3.3.2. DDSM DDSM provides orderly conditions to attain the goals of DSM tidily. The DDSM methods aim to equilibrate energy via control of efficiency. The DDSM involves the onus of clients and their direct participation in IRP as a distinguished resource which has been identified as DSI in the literature. According to [17], the term ‘ELM’ is increasingly being substituted with the term ‘DR’ and DSM is being transformed to DSI [20–27]. For instance, Ref. [23] has described the ‘variable service subscription (VSS)’ to integrate the resources caused by DSM into the power system and electricity market operations. It details a demandside implementation method and the relevant smart grid infrastructure to prevalently accomplish DSI.

3.2.2.3. Modalities of ENCON. Similar to that was described for DSM modalities, there are two modalities of ENCON called ‘static ENCON (SENCON)’ and ‘dynamic ENCON (DENCON)’. The comparison of SENCON and DENCON is shown in Fig. 2. 3.2.2.3.1. SENCON. SENCON is founded on ECM to reduce the loss. Thus, energy audit and energy saving are two mechanisms of SENCON.

3.3.2.1. Classification of DDSM methods. The DDSM methods are divided into two general groups including ‘DELM’ and ‘DENCON’. The classification of DDSM in the proposed DSM theoretical framework is presented in Fig. 4.

3.2.2.3.2. DENCON. DENCON is founded on EEM to reduce the loss. Thus, energy audit and energy recovery are two modalities of DENCON.

3.3.2.1.1. DELM methods. There are two types of DELM: ‘OPU’ and ‘DR’. The borderline between DSM of traditional regulated power systems and DSM of restructured power systems is made by these two types of DELM. The DELM has been subjected to a transition from administration measures (OPU) to market measures (DR). Fundamentally, OPU can be considered as nonmarket-based DELM and DR can be considered as market-based DELM. Theoretically, OPU involves executive and out-of-market coordination but DR is in favor of market regulation. Both DR and OPU follow the goals of ELM as well as a set of objectives of power system operation. The objective is set on social benefits and reduction of gasses emission in DENCON programs. The classification of DELM in the proposed DSM theoretical framework is shown in Fig. 5.

3.3. Taxonomy of DSM methods The managerial measures are divided into two general groups on the basis of DSM strategies i.e. SDSM and DDSM. 3.3.1. SDSM SDSM aims to equilibrate energy via control of energy consumption. SDSM comprises the policies and activities to encourage endusers to change their normal energy consumption pattern but the complete implementation of these methods depends on the customers’ volition eventually. Furthermore, the end-use customers have no onus in SDSM but the motivation can be created through static management such as educational programs and advertisements. The objective is generally set on reduction of energy cost for end-users in out-of-order conditions.

3.3.2.1.1.1. OPU methods. Mostly, the term ‘OPU’ refers to the managerial measures with out-of-market characteristics considering the onus of clients. OPU is defined as the regulation of load demand by adopting administrative, economic, technological and other measures which lead clients to effectively utilize electricity via guarantee in critical condition such as power supply shortage or when reliability is jeopardized. OPU has been introduced on the basis of extending the theoretical infrastructure of DSM, from ‘IRP via SDSM (IRP/SDSM)’ to ‘power system operation via DDSM (DSI/DDSM)’. The main purpose of OPU is the equilibration of power supply and demand, and the encountering of events. The OPU has been studied in the literature

3.3.1.1. Classification of SDSM methods. The SDSM methods are divided into two general groups including ‘SELM’ and ‘SENCON’. The classification of SDSM is presented in Fig. 3. 3.3.1.2. Implementation of SDSM methods. The services of power system are provided by utilities for the clients. The clients and utilities may independently treat to alter the pattern of demand [2] but deliberate intervention of the utility is a proviso for a measure to be classified as DSM [1]. The SDSM involves the managerial measures to predispose the implementation of programs that make the end-use customers inclined to appropriate attitude. The infrastructure of SDSM is provided on the basis of the policies set by governments (and/or utilities), but the end-use customers are empowered on applying them without any onus. Indeed, administrative measures including laws, regulations, acts and other measures provide infrastructure to spread the technologies, products, and available appliances and the clients may employ them voluntarily. Thus, there is no warranty of implementation by clients but the propellant can be made through cultural and educational programs. The main characteristic of SDSM is the definition of the concepts and methods under out-of-order conditions. For instance, ‘the scheming of high efficiency fluorescent lighting program by government and substitution of lighting appliances by commercial consumers’ and ‘refrigerator labelling and standards Program’ can be taken into account as some SDSM methods. Also,

Fig. 2. Classification of Energy Conservation in the proposed DSM theoretical framework.

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[78–80]. For instance [81], has accomplished OPU through an intelligent management system. The suggested system can established real-time communication, and meet the necessity of dynamic electricity pricing to attain energy efficiency, energy-saving targets and emission reduction. The market concept is accompanied by OPU in [81] tacitly. An automatic generation method of optimization scheme for OPU has been designed in [82] in order to optimize the resource allocation and maximize the social benefits simultaneously. In fact, OPU was introduced when changes in the methods of SDSM was necessary to embed the guarantee. For instance, the concept of incentive payments was introduced. Thus, SDSM was transmuted to OPU but at the same time the concept of power system restructuring influenced this transmutation and OPU was substituted with DR rapidly. However, OPU has a specific role in the theoretical framework proposed in the present paper. The OPU is divided into three groups. The classification assimilates LPC objective on the basis of this fact that the most effective state of consumption will occur if LF is maximum and quantity of consumption is minimum. First group is called ‘strategic saving’ which aims to decrease the total energy consumption in a certain time period and involves peak clipping and strategic conservation mechanisms. Second group is called ‘strategic productivity’ which aims LPC without any change in energy requirements and involves load shifting and flexible load shape mechanisms. Third group is called ‘strategic transfusion’ which aims LPC associated with energy growth and electrification. This type of OPU increases the total energy consumption in a certain time period as well as increases the LF and involves valley filling and strategic load growth mechanisms. 3.3.2.1.1.2. DR methods. Fundamentally, DR refers to change in electricity consumption in response to a stimulator that motivates enduse customers. The stimulators, from customer's viewpoint, are some factors that can reduce electricity cost on bill. DR has been defined as “alternations in electricity usage of customers from their usual consumption patterns in response to changes in the electricity price, or in response to incentives designed to influence electricity utilization

Fig. 3. Classification of SDSM in the proposed DSM theoretical framework.

Fig. 4. Classification of DDSM in the proposed DSM theoretical framework.

Fig. 5. Classification of DELM in the proposed DSM theoretical framework.

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3.3.2.1.2. DENCON methods. The objective of DENCON methods is equilibration of energy via enhancement of energy efficiency. The ‘energy recovery” and energy audit is two modalities of DENCON. It is worth to be mentioned that ‘energy audit’ is a hybrid modality in the proposed DSM theoretical framework. Ref. [147] has critically reviewed the DENCON with economic perspective.

at critical times characterized by high wholesale market prices or when the reliability is imperiled” in [15]. Mostly, this basic definition has been considered as a standard definition of DR in the literature (e.g [34,36,83–87].). Some samples of DR concepts can be seen in the 1980's (e.g [9,88,89].). In recent years, after power system restructuring, the measures of DDSM is being reformed to the regular and systematic methods. Thus, in past two decades DR programs have been introduced as modern methods of DSM in order to alter the electricity consumption patterns of clients in the power systems [9,13,15,17,34–43,48,81,83,85,86,90–145]. Generally, two factors have effective role in motivation of clients to accept DR programs: First factor is the change in electricity price and second factor is incentive schemes in order to lead the clients to reduce consumption in critical conditions. The critical conditions occur when either wholesale market price is high or system isn’t in secure state or the reliability is jeopardized. If the wholesale market price is high, DR programs will apply to reflect the spot prices to the clients through their direct participation in power market or some indirect ways. If reliability is jeopardized, DR programs will aim to influence the clients to reduce consumption according to previous contracts. Responsibility of such clients may lead to incentive remuneration if they respond or penalty if they don’t respond. Depending on load variation in different hours and the changes in generation and transmission capacity limitations due to contingencies, the electricity price is extremely volatile. Moreover, the expensive power plants with high running cost must be committed during peak load hours which increases the electricity price in these time periods. Without DR programs, end users will pay with a fixed price called retail electricity price regardless of real wholesale electricity price i.e. the average of generation, transmission and distribution costs. The difference between the real wholesale electricity price and clients’ payments decreases the efficiency of resources because end-use customers don’t face with the spot electricity prices and haven’t enough stimulus to alter their consumption patterns. One of the important roles of DR is modification of market clearing price (MCP). The normal consumption pattern of customers is usually depicted by high consumption at peak demand hours and low consumption at off-peak demand hours which causes a gap between MCP of peak and off-peak demand hours. DR is considered to mitigate the damages caused by this gap. DR is also able to avoid market power by raising electricity price over the generation cost. Therefore, DR improves the performance of electricity markets. Moreover, DR helps to reliability of the system. The consumers may participate to prevent violation of limitations and keep the system in normal state. Without DR programs, utility has to perform the shedding options to curtail some loads but implementing DR programs may reduce the probability of forced outages and their unfavorable outcomes which imposes extra costs and discomfort to the clients. On the other hand, since clients encounter the reasonable prices and respond, system operator is able to apply the price signals to fulfill the constraints because the clients reduce their consumption at peak period which lowers the price and releases a part of generation capacity in these periods. Making infrastructures to enhance the ability of demand-side to respond to the price signals benefits the clients who participate in the electricity market. Moreover, participation of demand in electricity markets is offered by electricity retailers so that industrial loads can compete in the electricity markets [146]. The effect of alternations in supply-side such as generator outage or market power on electricity price and the necessity of DR in order to modify the price have been analyzed in [146]. Classification of DR methods have been done from various viewpoints in the literature (e.g. in [34,37,83,84]). From the power system operator perspective, DR can be classified into two general categories: ‘DR without dispatch capability’ and ‘DR with dispatch capability’. The taxonomy of DR is separately explicated in Section 3.4.

3.3.2.2. Implementation of DDSM methods. The DDSM involves the managerial measures to predispose the implementation of programs that make the end-use customers liable to appropriate attitude. The end-use customers obligated in DDSM programs. The main characteristic of DDSM is the definition of the concepts and methods considering the onus of customers. The ‘incentive payments to industrial customers for energy efficiency improvement by inspecting the process i.e. auditing such as insulation improvement of a boiler in an industrial consumer considering the relevant regulations’ and ‘dynamic electricity pricing’ are examples of DDSM.

3.4. Classification of DR methods The DR can be classified into two general categories: ‘DR without dispatch capability’ and ‘DR with dispatch capability’. 3.4.1. DR methods without dispatch capability This category of DR methods involves the methods based on electricity pricing for end-use customers considering the objectives of DDSM. Theoretical frame of these measures can be described by financial traits of restructured power systems. Regarding the obligation of retailers, which is usually determined in bilateral contracts, they must provide the required energy to their customers by purchasing electricity from the wholesale market or any other way such as employing distributed generations. Since the electricity price is extremely varying, selling the electricity to the customers with fixed financial rates makes high risk for retailers because they face the volatile electricity prices in the wholesale market. The financial measures of “DR methods without dispatch capability” result in different tariffs proportional to the generation cost for various time periods. These time-varying rates motivate or encourage consumers to alleviate their consumption at peak load periods to attain the objectives of DELM. These methods have been reported as “PriceBased Demand Response Programs” in [15,37,39,83,101], “Rate-based DR Programs” in [36] and “Time-based Programs” in [90] and “Dynamic Pricing Programs” in [14]. Ref. [14] has described DSM methods with emphasis on “Price-Responsive Programs”. The main characteristic of these methods is independency from the structure of grid. Hence, they can be implemented in traditional regulated power systems as well as restructured power systems. Three different types of DR methods without dispatch capability discussed in the following. 3.4.1.1. Real-time pricing (RTP). Theoretically, the most effective strategy to persuade the clients in alternation of their consumption pattern is the selling of the electricity to end-use customers with realtime power market prices in order that they will face with actual wholesale prices. Thus, the electricity price for end users is unsteady and fluctuates hourly to reflect the real generation cost of each hour directly (in one-sided cost-based power pools) or implicitly (in onesided price-based power pools). Practically, the retailers are informed by price signals on a day-ahead or hour-ahead basis. Therefore, they can participate in retail markets with lower risk so that real-time prices can become more efficient. An analytical method has been addressed in order to mathematically describe the cost saving potential of RTP method in [13]. R. Sioshansi has discussed about the effect of RTP on reducing the real-time re-dispatch costs imposed on account of dayahead wind power forecasting errors in [94]. RTP can be implemented as a mandatory or voluntary program [95]. RTP may be mandatory for 373

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3.4.1.3.1. Fixed period critical peak pricing (FP-CPP). In this type of CPP, the maximum number of the days per year and the time spans in which end-use customers will be invited to respond are predetermined. Since the time of critical peak periods cannot exactly predetermined, the customers are prevalently informed on the preceding day.

industrial and large commercial loads. Although these customers encounter price volatility, bilateral contracts can mitigate the relevant risk. However, because of real-time electricity price fluctuations, RTP incurs uncertainty inherently. Regardless of these uncertainties, RTP is the effective method of DR without dispatch capability. Substantially, RTP follows all strategies of DELM. C. Zhi et al. have appraised the real-time price-based DR for residential loads in order to minimize the expected payment for one day [104]. Ref. [110] has proposed a RTP scheme in the direction of LPC in smart grids in which the retailers can cope the uncertainty of customers’ responses. Ref. [115] has proposed a control strategy that employs RTP signals for changing the set-point temperature to control HVAC loads. A DR approach has been proposed in [130] to achieve the efficacious load control of heterogeneous devices in response to the RTP data. A RTP approach has been suggested in [148] using stochastic algorithm to show the significant advantages of RTP. The concept of RTP has been extended to RTP+ in [149] to find the optimized hourly rates.

3.4.1.3.2. Critical peak rebates (CPR). In this type of CPP, fixed rates are specified for different time periods but the discounts on the bills are considered for end-use customers who reduce their electricity consumption during critical peak periods.

3.4.2. DR methods with dispatch capability ‘DR with dispatch capability’ aims to attain the DDSM objectives through financial incentives and special market mechanisms so that the demand-side resources will be incorporated into power system operation. The ‘DR methods with dispatch capability’ give incentive of response to the participants. Therefore, these methods of DR are usually called “Incentive-based programs” e.g. in [15,37,43,83,84,90,93,101]. These methods allow the demand-side to participate in the designed markets with the same effectiveness of the suppliers. The incentives for customers who respond to the programs are three types:

3.4.1.2. Time of use tariff (TOU). The imitation of real-time wholesale electricity spot price to determine electricity rate for end-use customers, needs appropriate equipment so that the end-use customers can respond to the instantaneous variations. For instance, an electricity pricing strategy has been proposed in [110] to correct LF through RTP whose infrastructure has been contrived in the smart grid. Modified method of RTP called Time of use (TOU) divides the entire day, season or year into some time blocks to apply the different tariffs specified for each time block [10,12,90,97,101,108,112,121,123,150– 152]. These tariffs reflect the average cost of power generation, transmission and distribution during the time periods. Thus, the electricity rate will be low for the slight load periods, mild for the intermediate load periods, and high in the peak load period. Therefore, in TOU method, the real-time price fluctuation of the wholesale market is mitigated for the demand-side in the form of piecewise linear approximation. The TOU has been introduced as the most common price-based method of DR in [101]. The main issue of TOU is determination of optimal rates for the time blocks. TOU method needs special meters that can register the consumption quantities in different time blocks with individual tariffs. Ref. [152] has proposed a TOU approach to design the fundamental window patterns of TOU for residential customers by clustering techniques.

• • • • • •

Rebating on customers’ electricity bill Bill credit i.e. credit of consumption Incentive payments separated from the bill on the basis of previous contracts Moreover, these methods penalize the clients who sign up but neglect to respond or fulfill their contractual onuses. The penalties for customers that do not respond are three types: Increment of rate when events are declared and customers do not respond Decrease the remainder of credit Rejection from DR programs

These methods can be divided into two general groups: ‘DR programs with reliability scope’ and ‘DR programs with economic scope’. 3.4.2.1. DR programs with reliability scope. The reliability is an important issue in power system operation which has been addressed in [21,63,157]. Gellings et al. have referred to the reliability of the load management equipment in [63] where the assessment of the operator on the reliability of the load management system has been emphasized. Goel et al. have proposed a method in [157] to determine the loadshifting in order to enhance the reliability which mitigates the nodal price volatility. The DR programs with reliability scope provide better conditions for system operator to enhance reliability of the power system. These programs are three types:

3.4.1.3. Critical peak pricing (CPP). Although RTP isn’t applicable for all end users, the encountering of end users with real wholesale prices is very important in peak periods especially. The CPP is applied so that the clients can meet the real wholesale electricity prices in critical times. CPP involves pre-specified high electricity prices for critical peak periods which occur in some days of a year by system contingencies or high wholesale market spot prices. CPP rates may be imposed over the TOU tariffs with higher rates in critical peak periods [14,15]. In practice, the clients involved in CPP program (CPP participants) receive price discounts during noncritical peak periods. Ref. [153] have compared both CPP and TOU rates to analyze annual costs and greenhouse gas emissions. Response to CPP has been described by a matrix for price-elasticity of demand in [154] on the basis of an analysis for tariff scheme and CPP implementation process. Refs. [14,83,84] have addressed different types of CPP. Sometimes the time horizon of CPP can be extended. Thus, two methods called “Extreme Day Pricing” and “Extreme Day CPP” can be considered [14,84]. The mentioned references have regarded these methods as two generic methods of dynamic pricing but in this paper we regard them as CPP with extended critical peak time span. Two types of ‘CPP without dispatch capability’ can be employed [155,156]:

3.4.2.1.1. Available demand-side resource capacity control (ADSRCC). Implementation of DR programs creates some resources on the demand-side which can be regarded as virtual resources. The capacity of these resources is available for the system operator. The system operator can control such capacity through ADSRCC programs. This control may be applied directly or indirectly. Thus, there are two distinct types of ADSRCC: 3.4.2.1.1.1. Direct ADSRCC. This control method is accomplished via special equipment with advanced technologies (such as advanced meters, communication devices, etc.) or capacity markets.

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get additional incentives on their bill. At least in shortage conditions, the penalty needs to be set at a level above the spot electricity price to motivate the participants [174]. The system operator identifies this customers and continually checks them and evaluates their readiness for capability of self-load curtailment. Accordingly, this method is considered as a type of Direct ADSRCC. 3.4.2.1.1.2. Indirect ADSRCC. These programs are implemented in two ways: 3.4.2.1.1.2.1. Contractual indirect load control (CILC). The most common form of ADSRCC is interruptible/curtailable contracts (e.g [93,103,188–197].). In this contracts, there is the obligation of enrollers for responding in the form of load curtailment when they receive signals from utility. If they respond in critical times, they will get incentive, otherwise they will be penalized. Thus, the participants provide a typical spinning reserve in the power system. The main specification of CILC is voluntary enrollment of clients and self-loadshedding. The interruptible loads can improve the operation cost and guarantee the security of the power system [196]. The CILC programs are often designed for industrial and large commercial customers and “selling shutting down by industrial loads” is a common technique pertaining to these programs. The optimized interruption strategy is addressed in [194] by developing a model for determining the electricity price to value the interruptible load contracts from the retailer's viewpoint. 3.4.2.1.1.2.2. Indirect control via CPP. These programs merge CPP and technological measures [198–216]. Ref. [14] has called this method as “CPP with enabling technologies”. In these programs, response to the critical peak prices is accomplished automatically. According to [14], in this method, TOU tariffs are normally predetermined but much higher rates are imposed during critical hours. The results of such plans which has been implemented by some utilities show that this method can attain the objectives of DDSM better than CPP without automatic response. According to [105], the CPP with load control (CPP-LC) is a combination of TDLC programs and CPP. The different types of ADSRCC is shown in Fig. 6. This method needs individual load automation equipment to automatically regulate power consumption when critical peak prices are proclaimed [155]. Two types of CPP with dispatch capability can be considered [155,156]: 3.4.2.1.1.2.2.1. Variable period CPP (VP-CPP). In this type of CPP, the time, the duration of the interval and the days in which the rate will be raised are not specified and the end-use customers are usually notified by little warning. 3.4.2.1.1.2.2.2. Variable peak pricing (V-PP). This type of CPP is based on a TOU strategy in which the tariff of peak period is flexible. The critical peak price will be determined on the basis of the real-time wholesale electricity price in the same day or in terms of the locational marginal prices. 3.4.2.1.2. Voluntary response programs. These programs are based on acts and regulations in which incentive payments to consumers who reduce their consumption during system events are specified. Each customer can sign up but the response is absolutely voluntary. The participants who respond the programs will get incentive payments in terms of measured load reductions, otherwise, they will be rejected from DR programs. Accordingly, if the participants are notified, they may ignore the incentive payments and consume whatever they want. These programs can be implemented in a microgrid in which all components are bestowed from the microgrid profit.

Fig. 6. Different types of ADSRCC in the proposed DSM theoretical framework.

3.4.2.1.1.1.1. Technological Direct Load Control (TDLC). By TDLC programs the operator can remotely control electricity utilization of end users. Thus, the system operator can adopt preventive control to bring system back to the secure state or to maintain the system in normal state. Also system operator can adopt corrective control to bring the system back to the normal state. In this method, technological measures are employed by which the operator can remotely switch some apparatus on/off or alternates some clients’ electric appliances (e.g. air conditioner, water heater) with short notice [158–168]. The TDLC is chiefly applied for residential or small commercial loads [47,98]. The main characteristic of TDLC is voluntary enrollment and mandatory response of end-use customers by which the system operator are able to perform the proper activities of reliability and security. The designing appropriate incentives for voluntary participation of customers is addressed in [169]. TDLC is the best method of DR with dispatch capability to increase the reliability of the system directly. According to [98], residential participants of TDLC programs are more than other DR programs in North America. Ref. [170] has proposed a quantitative approach to evaluate the benefits of TDLC for urban areas. TDLC programs cause that a part of total load can be directly disposed by system operator as a virtual resource. Instead of the mandatory response of TDLC, incentive will be considered for participants in the form of bill credit. Moreover, TDLC can be applied to attain the DELM objectives. Peak-clipping and/or flexible load shape can be conveniently implemented by TDLC programs. The advanced metering infrastructure (AMI) can provide suitable base for data collection and system control [47]. Ref. [171] have addressed the role of smart grids in DELM. An IRP model has been presented in [47] which has been incorporated TDLC in the micro-grid operation planning in order to implement the load-shifting and peak-clipping strategies. Consumer distrust may perform an important role in the acceptance of new technology or participation in TDLC programs [172]. 3.4.2.1.1.1.2. Direct control through capacity markets. Capacity market is often in the form of a bilateral trade model in which participants are the utilities and specific clients that have the capability of consumption reduction when events occur [173–187]. This capability is recognized by the independent system operator and proposed to the clients. If clients enroll, the utility will pay them a quantity separated from their bill (third form of incentive). These clients undertake to reduce their consumption when they are notified. The concept of this method is based on providing a typical insurance to enhance the reliability with regard to contingencies emergence [93]. In practice, the reduction may not be required and these contracts are regulated to enhance the level of power system reliability. The incentives given to the consumers for readiness of self-loadcurtailment are called “Capacity Payments”. If these clients did not respond, they will be highly penalized. Otherwise, the consumers will

3.4.2.1.3. Available demand-side reserve management (ADSRM). These programs mainly enable the clients to bid the load curtailments as non-spinning reserve resources into the ancillary services market. The designing, analysis, and assessment of the

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[3] Gellings CW, Barron W, Betley FM, England WA, Preiss LL, Jones DE. Integrating Demand-side Management Into Utility planning. Power Syst IEEE Trans 1986;1:81–7. [4] Gellings CW, Chamberlin J. "Demand-side management, Energy Efficiency and Renewable Energy Handbook, Second Edition edited by D. Yogi Goswami, Frank Kreith (Chapter 15); 1988. p. 289–310. [5] Gellings CW, Smith WM. Integrating demand-side management into utility planning. Proc IEEE 1989;77:908–18. [6] Nadel S. Utility demand-side management experience and potential-a critical review. Annu Rev Energy Environ 1992;17:507–35. [7] Goldman CA, Kito M. Review of demand-side bidding programs: impacts, costs and cost-effectiveness. Energy & Environment Division, Lawrence Berkeley Laboratory, University of Calif; 1994. [8] Goldman CA, Kito MS. Review of US utility demand-side bidding programs. Uti Policy 1995;5:13–25. [9] Daryanian B, Tabors RD, Bohn RE. Optimal demand-side response to electricity spot prices for storage-type customers. IEEE Trans Power Syst 1989;4(3):897–903. [10] Sheen JN, Chen CS, Wang TY. Response of large industrial customers to electricity pricing by voluntary time-of-use in Taiwan. IEE Proc - Gener Transm Distrib 1995;142:157–66. [11] Roos JG, Kern CF. "Modelling customer demand response to dynamic price signals using artificial intelligence,"inEighth International Conference on Metering and Tariffs for Energy Supply (Conf. Publ. No. 426); 1996 p. 213–217. [12] Mostafa Baladi S, Herriges JA, Sweeney TJ. Residential response to voluntary time-of-use electricity rates. Resour Energy Econ 1998;20:225–44. [13] Roos JG, Lane IE. Industrial power demand response analysis for one-part realtime pricing. Power Syst IEEE Trans 1998;13:159–64. [14] River C. Primer on Demand-Side Management With an emphasis on priceresponsive programs; 2005. [15] DOE. Benefits of demand response in electricity markets and recommendations for achieving them; 2006. [16] Boshell F, Veloza OP. Review of developed demand side management programs including different concepts and their results, in Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES; 2008. p. 1–7. [17] Lampropoulos I, Kling WL, Ribeiro PF, van den Berg J. History of demand side management and classification of demand response control schemes, in Power and Energy Society General Meeting (PES), 2013 IEEE; 2013. p. 1–5. [18] Behrangrad M. A review of demand side management business models in the electricity market, Renew Sustain Energy Rev, 47; 2015. 270–283. [19] Sharifi R, Fathi SH, Vahidinasab V. A review on Demand-side tools in electricity market. Renew Sustain Energy Rev 2017;72:565–72. [20] Baitch A, Chuang A, Mauri G, Schwaegerl C. International perspectives on demand-side integration, in Proceedings of the 19th International Conference on Electricity Distribution (CIRED), Vienna; 2007. [21] Chuang AS. Demand-side Integration for System Reliability, in Power Tech, 2007 IEEE Lausanne; 2007. p. 1617–1622. [22] Chuang A, Gellings C. Demand-side integration in a restructured electric power industry, CIGRE, number Paper C6-105, Session; 2008. [23] Chuang A, Gellings C. Demand-side integration for customer choice through variable service subscription, in Power & Energy Society General Meeting. PES '09. IEEE; 2009. p 1–7. [24] De Ridder F, Hommelberg M, Peeters E. Four potential business cases for demand side integration, in Energy Market, 2009. EEM 2009. 6th International Conference on the European; 2009. p. 1–6. [25] De Ridder F, Hommelberg M, Remans K, Peeters E. Demand side integration: exploring the flexibility of a cluster. Int J Distrib Energy Resour 2009;5:295–314. [26] De Ridder F, Hommelberg M, Peeters E. Demand side integration: four potential business cases and an analysis of the 2020 situation. Eur Trans Electr Power 2011;21:1902–13. [27] Silvestro F, Baitch A, Pilo F, Jensen BB, Fan M, Pisano G. , et al., Demand side integration aspects in active distribution planning. IET Conference Proceedings, 1475–1475. Available: http://digital-library.theiet.org/content/conferences/10. 1049/cp.2013.1254; 2013. [28] Ming Z, Song X, Mingjuan M, Lingyun L, Min C, Yuejin W. Historical review of demand side management in China: management content, operation mode, results assessment and relative incentives. Renew Sustain Energy Rev 2013;25:470–82. [29] Gelazanskas L, Gamage KAA. Demand side management in smart grid: a review and proposals for future direction. Sustain Cities Soc 2014;11:22–30. [30] Harper M. Review of Strategies and Technologies for Demand-Side Management on Isolated Mini-Grids, ed; 2014. [31] Warren P. A review of demand-side management policy in the UK. Renew Sustain Energy Rev 2014;29:941–51. [32] Li B, Shen J, Wang X, Jiang C. From controllable loads to generalized demandside resources: a review on developments of demand-side resources. Renew Sustain Energy Rev 2016;53:936–44. [33] Abdelaziz EA, Saidur R, Mekhilef S. A review on energy saving strategies in industrial sector. Renew Sustain Energy Rev 2011;15:150–68. [34] Balijepalli VSKM, Pradhan V, Khaparde SA, Shereef RM. Review of demand response under smart grid paradigm, in Innovative Smart Grid Technologies India (ISGT India); 2011 IEEE PES; 2011. p. 236–243. [35] Aghaei J, Alizadeh M-I. Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sustain Energy Rev 2013;18:64–72. [36] Siano P. Demand response and smart grids—A survey. Renew Sustain Energy Rev 2014;30:461–78.

markets in these programs have been mostly studied in the literature [91,217–230]. In [91] the role of DR in ancillary services markets has been described. The participants must have swift response capability. As a result the participants are paid on the basis of the market price because of being on standby. If the participants are called by the system operator and respond, they will be paid on the basis of real wholesale electricity market. The most important property of this method is encouragement of the large consumers to employ situ generations because of the benefits of participation in the market.

3.4.2.2. DR programs with economic scope. These programs are designed to modify the power market price and provide the clients’ entrance to the buyback markets in which customers are able to bid a part of their purchased electricity with upper prices [231,232]. Practically, the retailers bid these curtailable loads to the market operator after the settlement of the wholesale power market. If the bids are accepted, the retailers must accomplish their obligation. Therefore in retail market the consumers who are called and curtail their load, will be paid on the basis of the market clearing price. Consequently, it is possible to modify the performance of economic framework in the restructured power systems. Moreover, these programs encourage the large consumers to directly participate and bid their load curtailments into the wholesale electricity market. This method is the most efficient method of DR with dispatch capability because it provides a business opportunity for customers and makes stimulus of respond for participants conveniently, but it needs appropriate infrastructure in the deregulated energy environment. There is another style to implement this method. In this style, utility declares a reference price and the customers determine the amount of load which they are willing to curtail under the reference price. If the offered load curtailments are accepted, the customers have to reduce electricity consumption, otherwise, they will be penalized. A model has been proposed in [231] to optimize the demand-side bids and determine the MCP. A stochastic framework has been proposed in [232] to develop a joined active and reactive power market in smart grids in which the aggregators on behalf of price-responsive loads can participate in a demand buyback program.

4. Conclusion In this paper, the basic concepts, phrases, general subjects and practical methods of DSM were reviewed by reconsidering the DSM theoretical framework with three characteristics: presentation of exact definitions for the concepts, a novel classification of DSM methods, and description of methods and their objectives on the basis of arranged theoretical frame. The DSM methods was categorized into static and dynamic measures with clarified scopes. The strategies, modalities, mechanisms, and objectives were clearly defined. The DSM subjects have been highlighted from power system operator's point of view. The evolution of DSM theory as well as state of the art concepts of DSM were aggregated to organize a novel theoretical frame with unequivocalness in terminology to unify the concepts and techniques developed in the literature. Moreover, this paper has aggregated former definitions related to DSM theory from the literature and hence, involves all of the concepts and known methods in evolution of DSM. The comprehensive classification of DSM methods presented in this paper can cover all known DSM approaches with a unified terminology. References [1] Gellings CW. The concept of demand-side management for electric utilities. Proc IEEE 1985;73:1468–70. [2] Gellings CW. The special section on demand-side management for electric utilities. Proc IEEE 1985;73:1443–4.

376

Renewable and Sustainable Energy Reviews 80 (2017) 367–379

A. . Meyabadi, M.H. Deihimi

Energy 2007;32:1326–33. [72] Honma S, Hu J-L. Total-factor energy productivity growth of regions in Japan. Energy Policy 2009;37:3941–50. [73] Chang T-P, Hu J-L. Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Appl Energy 2010;87:3262–70. [74] Adhikari D, Chen Y. Energy productivity convergence in Asian countries: a spatial panel data approach. Int J Econ Financ 2014;6:p94. [75] Fleiter T, Schleich J, Ravivanpong P. Adoption of energy-efficiency measures in SMEs—An empirical analysis based on energy audit data from Germany. Energy Policy 2012;51:863–75. [76] Dongellini M, Marinosci C, Morini GL. Energy audit of an industrial site: a case study. Energy Proc 2014;45:424–33. [77] Wikler G, Faruqui A, Gellings CW, Seiden K. The potential for energy efficiency in electric end use technologies. Power Syst IEEE Trans 1993;8:1351–7. [78] Chen H-Y, Liu X-C. Fine management process of orderly power utilization work. Dianli Xuqiuce Guanli (Power Demand Side- Manag) 2010;12:30–3. [79] Wang J, Hu Z-Y. Optimization and application of the management mode of orderly electricity consumption. Power Demand Side- Manag 2012;3:014. [80] Wu J, Zhang K, Wang J, Wu D, Hu L. Decision support system for orderly power utilization based on delicacy management. Zhejiang Electr Power 2013;32:59–63. [81] Zhang X, Lu J, Sun H, Ma X. Orderly Consumption and Intelligent Demand-side Response Management System under Smart Grid," in Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific; 2010. p. 1–4. [82] Huang J-J, Zuo Q-I, Mu F-l. Automatic Generation Method of Optimization Scheme for Orderly Power Utilization Based on Genetic Algorithm," in Dependable, Autonomic and Secure Computing (DASC), 2013 IEEE 11th International Conference on; 2013. p. 72–77. [83] Albadi MH, El-Saadany EF. Demand Response in Electricity Markets: An Overview, in Power Engineering Society General Meeting. IEEE; 2007. p. 1–5. [84] Albadi MH, El-Saadany EF. A summary of demand response in electricity markets. Electr Power Syst Res 2008;78:1989–96. [85] Chao H-P. An economic framework of demand response in restructured electricity markets. Holyoke, MA: ISO New England; 2009. [86] Aalami HA, Moghaddam MP, Yousefi GR. Modeling and prioritizing demand response programs in power markets. Electr Power Syst Res 2010;80:426–35. [87] Martini PD. DR 2.0: A Future of Customer Response; 2013. [88] Caramanis MC, Bohn RE, Schweppe FC. Optimal spot pricing: practice and theory. Power Appar Syst, IEEE Trans 1982;PAS-101:3234–45. [89] Aigner DJ. The welfare econometrics of peak-load pricing for electricity: editor's Introduction. J Econ 1984;26:1–15. [90] Aalami H, Yousefi GR, Moghadam MP. Demand Response model considering EDRP and TOU programs, in Transmission and Distribution Conference and Exposition, 2008. IEEE/PES; 2008. p. 1–6. [91] Schisler K, Sick T, Brief K. The role of demand response in ancillary services markets, in Transmission and Distribution Conference and Exposition, 2008. T & D. IEEE/PES; 2008. p. 1–3. [92] Chua-Liang S, Kirschen D. Quantifying the effect of demand response on electricity markets. Power Syst IEEE Trans 2009;24:1199–207. [93] Aalami HA, Moghaddam MP, Yousefi GR. Demand response modeling considering Interruptible/Curtailable loads and capacity market programs. Appl Energy 2010;87:243–50. [94] Sioshansi R. Evaluating the impacts of real-time pricing on the cost and value of wind generation. Power Syst IEEE Trans 2010;25:741–8. [95] Faria P, Vale Z. Demand response in electrical energy supply: an optimal real time pricing approach. Energy 2011;36:5374–84. [96] Khodaei A, Shahidehpour M, Bahramirad S. SCUC with hourly demand response considering intertemporal load Characteristics. Smart Grid IEEE Trans 2011;2:564–71. [97] He Y, Wang B, Wang J, Xiong W, Xia T. Residential demand response behavior analysis based on Monte Carlo simulation: the case of Yinchuan in China. Energy 2012;47:230–6. [98] Hedin M. Demand Response for Residential Markets: Direct Load Control, Timeof-Use, Critical Peak Pricing, and Peak-Time Rebate Programs for Residential Customers: Global Market Analysis and Forecasts; 2012. [99] Ikeda Y, Ikegami T, Kataoka K, Ogimoto K. A unit commitment model with demand response for the integration of renewable energies, in Power and Energy Society General Meeting, 2012 IEEE; 2012. p. 1–7. [100] Long Z, Bo Z. Robust unit commitment problem with demand response and wind energy, in Power and Energy Society General Meeting, 2012 IEEE; 2012. p. 1–8. [101] Mozafari B, Bashirvand M, Nikzad M, Solaymani S. A SCUC-based approach to determine time-of-use tariffs," in Environment and Electrical Engineering (EEEIC), 2012 11th International Conference on; 2012. p. 429–433. [102] Papavasiliou A, Oren SS. A stochastic unit commitment model for integrating renewable supply and demand response, in Power and Energy Society General Meeting, 2012 IEEE; 2012. p. 1–6. [103] Sahebi MM, Duki EA, Kia M, Soroudi A, Ehsan M. Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts. IET Gener Transm Distrib 2012;6(7):605–11. http://dx.doi.org/10.1049/iet-gtd.2011.0806. [104] Zhi C, Lei W, Yong F. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. Smart Grid IEEE Trans on 2012;3:1822–31. [105] Aghaei J, Alizadeh M-I. Critical peak pricing with load control demand response program in unit commitment problem. IET Gener Transm Distrib 2013;7(7):681–90. http://dx.doi.org/10.1049/iet-gtd.2012.0739.

[37] Vardakas JS, Zorba N, Verikoukis CV. A survey on demand response programs in smart grids: pricing methods and optimization algorithms. Commun Surv Tutor IEEE 2015;17:152–78. [38] Haider HT, See OH, Elmenreich W. A review of residential demand response of smart grid. Renew Sustain Energy Rev 2016;59:166–78. [39] Malik A, Ravishankar J. A review of demand response techniques in smart grids, in 2016 IEEE Electrical Power and Energy Conference (EPEC); 2016. p. 1–6. [40] Shan K, Wang S, Yan C, Xiao F. Building demand response and control methods for smart grids: a review. Sci Technol Built Environ 2016;22:692–704. [41] Zehir MA, Batman A, Bagriyanik M. Review and comparison of demand response options for more effective use of renewable energy at consumer level. Renew Sustain Energy Rev 2016;56:631–42. [42] Good N, Ellis KA, Mancarella P. Review and classification of barriers and enablers of demand response in the smart grid. Renew Sustain Energy Rev 2017;72:57–72. [43] Paterakis NG, Erdinç O, Catalão JPS. An overview of demand response: keyelements and international experience. Renew Sustain Energy Rev 2017;69:871–91. [44] Bertschi SF. Integrated resource planning and demand-side management in electric utility regulation: public utility panacea or a waste of energy. Emory Law J 1994;43:815. [45] Guan X, Luh PB. Integrated resource scheduling and bidding in the deregulated electric power market: new Challenges. Discret Event Dyn Syst 1999;9:331–50. [46] Hu Z, Han X, Wen Q. Integrated resource strategic planning and power demandside management. Springer; 2013. [47] Lan Z, Zheng Y, Wei-Jen L, Xiu Y, Yang F. Direct load control in microgrid to enhance the performance of integrated resources planning, in Industrial & Commercial Power Systems Tehcnical Conference (I & CPS); 2014 IEEE/IAS 50th; 2014. p. 1–7. [48] Mazidi M, Zakariazadeh A, Jadid S, Siano P. Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Convers Manag 2014;86:1118–27. [49] Zhu L, Yan Z, Lee WJ, Yang X, Fu Y, Cao W. Direct load control in microgrids to enhance the performance of integrated resources planning. IEEE Trans Ind Appl 2015;51:3553–60. [50] Kelly JR, Robinson GP. Electrical load management system, ed: Google Patents; 1980. [51] Gellings CW. IEEE PES load management working group. Power Eng Rev IEEE 1981;PER-1:7–8. [52] Gellings CW. Power/energy: demand-side load management: the rising cost of peak-demand power means that utilities must encourage customers to manage power usage. Spectr IEEE 1981;18:49–52. [53] Gellings CW, Taylor RW. Electric load curve synthesis - a computer simulation of an electric utility load shape. Power Appar Syst IEEE Trans 1981;PAS-100:60–5. [54] Comerford RB, Gellings CW. The application of classical forecasting techniques to load management. Power Appar Syst IEEE Trans 1982;PAS-101:4656–64. [55] Gellings CW, Johnson AC, Yatcko P. Load management assessment methodology at PSE & G. Power Appar Syst IEEE Trans 1982;PAS-101:3349–55. [56] Gellings C. Economic issues related to assessing load management in electric utilities the economic assessment subgroup chaired by Man-Loong (M. L.) Chan of the IEEE Load Management Working Group. Power Eng Rev IEEE 1983;PER3:52. [57] Schulte RP, Gellings CW, Johnson WA, Delgado RM, Stitt JR, Chamberlin JH, et al. Load management—how will operators want to use it. Power Eng Rev IEEE 1983;PER-3:56–7. [58] Delgado RM, Gellings CW. "LMITs" - load management innovative techniques. Power Eng Rev IEEE 1984;PER-4:45. [59] Flory J, Gellings CW. Utility Load management organization survey. Power Eng Rev IEEE 1984;PER-4:31. [60] Gellings CW. Distributed intelligence load control: yes or no? A report prepared by the Load Management Subcommittee. Power Eng Rev IEEE 1984;PER-4:30–1. [61] Berkowitz DG, Gellings CW. Glossary of terms related to load management, parts I and II. Power Eng Rev IEEE 1985;PER-5:35. [62] Gellings CW, Forgey HL. Integrating load management into utility planning. Power Eng Rev IEEE 1985;PER-5:38. [63] Gellings CW, Stickels T, Cromie D, Mak S, Hastings BF, Fernstrom G, et al. Load management equipment reliability. Power Eng Rev IEEE 1985;PER-5:25. [64] Gu W, Wu Z, Bo R, Liu W, Zhou G, Chen W, et al. Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: a review. Int J Electr Power Energy Syst 2014;54:26–37. [65] Gambini M, Vellini M. High efficiency cogeneration: electricity from cogeneration in CHP plants. Energy Proc 2015;81:430–9. [66] Li Y, Chang S, Fu L, Zhang S. A technology review on recovering waste heat from the condensers of large turbine units in China. Renew Sustain Energy Rev 2016;58:287–96. [67] Vukašinović V, Gordić D, Babić M, Jelić D, Končalović D. Review of efficiencies of cogeneration units using internal combustion engines. Int J Green Energy 2016;13:446–53. [68] Tomofuji D, Morimoto Y, Sugiura E, Ishii T, Akisawa A. The prospects of the expanded diffusion of cogeneration to 2030 – Study on new value in cogeneration. Appl Therm Eng 2017;114:1403–13. [69] Miketa A, Mulder P. Energy productivity across developed and developing countries in 10 manufacturing sectors: patterns of growth and convergence. Energy Econ 2005;27:429–53. [70] Fisher-Vanden K, Jefferson GH, Jingkui M, Jianyi X. Technology development and energy productivity in China. Energy Econ 2006;28:690–705. [71] Wang C. Decomposing energy productivity change: a distance function approach.

377

Renewable and Sustainable Energy Reviews 80 (2017) 367–379

A. . Meyabadi, M.H. Deihimi [106] Aghaei J, Alizadeh M-I. Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems). Energy 2013;55:1044–54. [107] Chaoyue Z, Jianhui W, Watson JP, Yongpei G. Multi-stage robust unit commitment considering wind and demand response uncertainties. Power Syst IEEE Trans 2013;28:2708–17. [108] de Sa R, Ferreira , Barroso LA, Rochinha Lino P, Carvalho MM, Valenzuela P. Time-of-use tariff design under uncertainty in price-elasticities of electricity demand: a stochastic optimization approach. Smart Grid IEEE Trans 2013;4:2285–95. [109] Hurley D, Peterson P, Whited M. Demand response as a power system resource. Synap Energy Econ Inc 2013. [110] Li Ping Q, Zhang YJA, Jianwei H, Yuan W. Demand response management via real-time electricity price control in smart Grids. Sel Areas Commun IEEE J 2013;31:1268–80. [111] Seung-Jun K, Giannakis GB. Scalable and robust demand response with mixedinteger constraints. Smart Grid IEEE Trans 2013;4:2089–99. [112] Wang Y, Li L. Time-of-use based electricity demand response for sustainable manufacturing systems. Energy 2013;63:233–44. [113] Alipour M, Zare K, Mohammadi-Ivatloo B. Short-term scheduling of combined heat and power generation units in the presence of demand response programs. Energy 2014;71:289–301. [114] Falsafi H, Zakariazadeh A, Jadid S. The role of demand response in single and multi-objective wind-thermal generation scheduling: a stochastic programming. Energy 2014;64:853–67. [115] Ji Hoon Y, Baldick R, Novoselac A. Dynamic demand response controller based on real-time retail price for residential buildings. Smart Grid IEEE Trans 2014;5:121–9. [116] Koliou E, Eid C, Chaves-Ávila JP, Hakvoort RA. Demand response in liberalized electricity markets: analysis of aggregated load participation in the German balancing mechanism. Energy 2014;71:245–54. [117] Li XH, Hong SH. User-expected price-based demand response algorithm for a home-to-grid system. Energy 2014;64:437–49. [118] Poudineh R, Jamasb T. Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement. Energy Policy 2014;67:222–31. [119] Ravindra K, Iyer PP. Decentralized demand–supply matching using community microgrids and consumer demand response: a scenario analysis. Energy 2014;76:32–41. [120] Zakariazadeh A, Jadid S, Siano P. Smart microgrid energy and reserve scheduling with demand response using stochastic optimization. Int J Electr Power Energy Syst 2014;63:523–33. [121] Aalami HA, Parsa Moghaddam M, Yousefi GR. Evaluation of nonlinear models for time-based rates demand response programs. Int J Electr Power Energy Syst 2015;65:282–90. [122] Ahmad A, Javaid N, Qasim U, Khan ZA. Demand response: from classification to optimization techniques in smart grid, in Advanced Information Networking and Applications Workshops (WAINA), 2015 IEEE 29th International Conference on; 2015. p. 229–235. [123] Bui V-H, Kim H-M, Song N-O. Applying demand response based on TOU and EDRP to optimal microgrid Operation. Int J Smart Home 2015;9:41–50. [124] Duo X, Peng L, Bo Z. Optimal scheduling of microgrid with consideration of demand response in smart grid, in Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on; 2015. p. 426–431. [125] Duong Tung N, Bao L Long. Risk-constrained profit maximization for microgrid aggregators with demand response. Smart Grid IEEE Trans 2015;6:135–46. [126] Faria P, Vale Z, Baptista J. Constrained consumption shifting management in the distributed energy resources scheduling considering demand response. Energy Convers Manag 2015;93:309–20. [127] Fotouhi Ghazvini MA, Faria P, Ramos S, Morais H, Vale Z. Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. Energy 2015;82:786–99. [128] Gao D-C, Sun Y, Lu Y. A robust demand response control of commercial buildings for smart grid under load prediction uncertainty. Energy 2015;93(Part1):275–83. [129] Ghasemi A, Mortazavi SS, Mashhour E. Integration of nodal hourly pricing in dayahead SDC (smart distribution company) optimization framework to effectively activate demand response. Energy 2015;86:649–60. [130] Hong SH, Yu M, Huang X. A real-time demand response algorithm for heterogeneous devices in buildings and homes. Energy 2015;80:123–32. [131] Neves D, Pina A, Silva CA. Demand response modeling: a comparison between tools. Appl Energy 2015;146:288–97. [132] Olamaei J, Ashouri S. Demand response in the day-ahead operation of an isolated microgrid in the presence of uncertainty of wind power. Turk J Electr Eng Comput Sci 2015;23:491–504. [133] Pourmousavi SA, Nehrir MH, Sharma RK. Multi-timescale power management for islanded microgrids including storage and demand response. Smart Grid IEEE Trans 2015;6:1185–95. [134] Richardson DB, Harvey LDD. Optimizing renewable energy, demand response and energy storage to replace conventional fuels in Ontario, Canada. Energy 2015;93(Part2):1447–55. [135] Safamehr H, Rahimi-Kian A. A cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program. Energy 2015;91:283–93. [136] Schreiber M, Wainstein ME, Hochloff P, Dargaville R. Flexible electricity tariffs: power and energy price signals designed for a smarter grid. Energy 2015;93(Part2):2568–81. [137] Zareen N, Mustafa MW, Sultana U, Nadia R, Khattak MA. Optimal real time cost-

[138] [139]

[140] [141] [142] [143] [144]

[145]

[146] [147] [148]

[149] [150] [151]

[152] [153] [154]

[155]

[156]

[157]

[158] [159]

[160] [161]

[162]

[163]

[164]

[165]

[166]

[167]

[168]

[169]

378

benefit based demand response with intermittent resources. Energy 2015;90(Part2):1695–706. Dong J, Xue G, Li R. Demand response in China: regulations, pilot projects and recommendations – A review. Renew Sustain Energy Rev 2016;59:13–27. Feuerriegel S, Neumann D. Integration scenarios of Demand Response into electricity markets: load shifting, financial savings and policy implications. Energy Policy 2016;96:231–40. Gils HC. Economic potential for future demand response in Germany – Modeling approach and case study. Appl Energy 2016;162:401–15. Hee-Tae R, Jang-Won L. Residential demand response scheduling with multiclass appliances in the smart Grid. IEEE Trans Smart Grid 2016;7:94–104. Leithon J, Teng Joon L, Sumei S. Battery-Aided demand response strategy under continuous-time block pricing. IEEE Trans Signal Process 2016;64:395–405. Li W, Xu P, Lu X, Wang H, Pang Z. Electricity demand response in China: status, feasible market schemes and pilots. Energy 2016;114:981–94. Nosratabadi SM, Hooshmand R-A, Gholipour E. Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy. Appl Energy 2016;164:590–606. Lima DA, Perez RC, Clemente G. A comprehensive analysis of the Demand Response Program proposed in Brazil based on the Tariff Flags mechanism. Electr Power Syst Res 2017;144:1–12. Kirschen DS. Demand-side view of electricity markets. Power Syst IEEE Trans 2003;18:520–7. Wirl F. Impact of regulation on demand side conservation programs. J Regul Econ 1995;7:43–62. Mahmud AA, Sant P, Tariq F, Jazani D. Empirical analysis of real time pricing mechanisms for demand side management: contemporary review, in 2016 Fifth International Conference on Future Generation Communication Technologies (FGCT); 2016. p. 11–16. Mays J, Klabjan D. Optimization of time-varying electricity rates. Energy J 2017;38. Henley A, Peirson J. Time-of-use electricity pricing: evidence from a British experiment. Econ Lett 1994;45:421–6. Mountain DC, Lawson EL. Some initial evidence of Canadian responsiveness to time-of-use electricity rates: detailed daily and monthly analysis. Resour Energy Econ 1995;17:189–212. Li R, Wang Z, Gu C, Li F, Wu H. A novel time-of-use tariff design based on Gaussian Mixture Model. Appl Energy 2016;162:1530–6. Wang Y, Li L. Critical peak electricity pricing for sustainable manufacturing: modeling and case studies. Appl Energy 2016;175:40–53. Zhang Q, Wang X, Fu M. Optimal implementation strategies for critical peak pricing, in 2009 6th International Conference on the European Energy Market; 2009. p. 1–6. Batlle C, Rodilla P. Electricity demand response tools: current status and outstanding issues, Working Paper IIT-08-006A. Prepared for: Special issue on incentives for a low-carbon energy future, European Review of Energy Markets; 2008. Greer M. U.S. electric markets, structure, and regulations. In: Greer M, editor. Electricity marginal cost pricing. Boston: Butterworth-Heinemann; 2012. p. 39–100, [Chapter 3]. Goel L, Qiuwei W, Peng W. Reliability Enhancement and Nodal Price Volatility Reduction of Restructured Power Systems with Stochastic Demand Side Load Shift, in Power Engineering Society General Meeting, 2007. IEEE; 2007. p. 1–8. Evora J, Hernandez JJ, Hernandez M. A MOPSO method for direct load control in smart grid. Expert Syst Appl 2015;42:7456–65. Kim YJ, Norford LK, Kirtley JL. Modeling and analysis of a variable speed heat pump for frequency regulation through direct load control. IEEE Trans Power Syst 2015;30:397–408. Cui Q, Wang X, Wang X, Zhang Y. Residential appliances direct load control in real-time using cooperative game. IEEE Trans Power Syst 2016;31:226–33. Haring TW, Mathieu JL, Andersson G. Comparing centralized and decentralized contract design enabling direct load control for reserves. IEEE Trans Power Syst 2016;31:2044–54. Kim YJ, Wang J. Power hardware-in-the-loop simulation study on frequency regulation through direct load control of thermal and electrical energy storage resources. IEEE Trans Smart Grid 2016, [vol. PP]. Luo F, Zhao J, Dong ZY, Tong X, Chen Y, Yang H, et al. Optimal dispatch of air conditioner loads in southern china region by direct load control. IEEE Trans Smart Grid 2016;7:439–50. Mortaji H, Ow Siew H, Moghavvemi M, Almurib HAF. Smart grid demand response management using internet of things for load shedding and smart-direct load control, in 2016 IEEE Industry Applications Society Annual Meeting; 2016. p. 1–7. Yao L, Damiran Z, Lim WH. Direct load control of central air conditioning systems using fuzzy optimization, in 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC); 2016. p. 1–6. Zhang C, Xu Y, Dong ZY, Ma J. Robust Operation of Microgrids via Two-Stage Coordinated Energy Storage and Direct Load Control, IEEE Transactions on Power Systems, vol. PP; 2016. p. 1-1. Zhang F, de Dear R, Candido C. Thermal comfort during temperature cycles induced by direct load control strategies of peak electricity demand management. Build Environ 2016;103:9–20. Zhang F, de Dear R. Application of Taguchi method in optimising thermal comfort and cognitive performance during direct load control events, Build Environ, 111; 2017. 160–168. Alizadeh M, Xiao Y, Scaglione A, Schaar Mvd. Incentive design for Direct Load

Renewable and Sustainable Energy Reviews 80 (2017) 367–379

A. . Meyabadi, M.H. Deihimi

[170]

[171] [172] [173] [174] [175]

[176] [177]

[178] [179] [180] [181] [182]

[183] [184] [185]

[186]

[187]

[188] [189] [190]

[191]

[192] [193]

[194]

[195]

[196]

[197]

[198] [199] [200]

[201]

[202] [203]

[204] Cappers P, Goldman C, Kathan D. Demand response in U.S. electricity markets: empirical evidence. Energy 2010;35:1526–35. [205] Faruqui A, Sergici S. Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 2010;38:193–225. [206] Herter K, Wayland S. Residential response to critical-peak pricing of electricity: California evidence. Energy 2010;35:1561–7. [207] Newsham GR, Bowker BG. The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: a review. Energy Policy 2010;38:3289–96. [208] Arnold GW. Challenges and opportunities in smart grid: a position article. Proc IEEE 2011;99:922–7. [209] Palensky P, Dietrich D. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform 2011;7:381–8. [210] Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, et al. A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inform 2013;9:28–42. [211] Bego A, Li L, Sun Z. Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems. Int J Energy Res 2014;38:728–36. [212] Huang L, Xu S, Wang X, Huo X, Zheng H. Dynamic optimized decision model of smart utilization for typical public building employing critical peak pricing, in 2016 China International Conference on Electricity Distribution (CICED); 2016. p. 1–5. [213] Jang D, Eom J, Jae Park M, Jeung Rho J. Variability of electricity load patterns and its effect on demand response: a critical peak pricing experiment on Korean commercial and industrial customers. Energy Policy 2016;88:11–26. [214] Kato T, Tokuhara A, Ushifusa Y, Sakurai A, Aramaki K, Maruyama F. Consumer responses to critical peak pricing: impacts of maximum electricity-saving behavior. Electr J 2016;29:12–9. [215] Khan AR, Mahmood A, Safdar A, Khan ZA, Khan NA. Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew Sustain Energy Rev 2016;54:1311–22. [216] Matsukawa I. Consumer Response to Critical Peak Pricing of Electricity and Conservation Requests, in Consumer Energy Conservation Behavior After Fukushima: Evidence from Field Experiments, ed Singapore: Springer Singapore; 2016. p. 19–43. [217] Bolton Zammit MA, Hill DJ, Kaye R. Designing ancillary services markets for power system security. Power Syst IEEE Trans 2000;15:675–80. [218] Kuzle I, Klaric M, Tesnjak S, Reactive power evaluation and market power in liberalized ancillary services market, in Electrotechnical Conference, 2004. MELECON 2004. Proceedings of the 12th IEEE Mediterranean; 2004. p. 1075– 1078, Vol.3. [219] Sun X, Tong M-G, Zhao Q-B, Lin H-Y, Liu M, Zeng M. A study on management model of secondary reserve for ancillary services market, Proc Csee, vol. 3, p. 005; 2004. [220] Wu T, Rothleder M, Alaywan Z, Papalexopoulos AD. Pricing energy and ancillary services in integrated market systems by an optimal power flow. Power Syst IEEE Trans 2004;19:339–47. [221] Papalexopoulos AD. Design of an Efficient Ancillary Services Market, in Power Engineering Society General Meeting, 2007. IEEE; 2007. p. 1–2. [222] Chin-Chung W, Wei-Jen L, Chin-Lung C, Hong-Wei L. Role and value of pumped storage units in an ancillary services market for isolated power systems— simulation in the taiwan power system. Ind Appl IEEE Trans 2008;44:1924–9. [223] Vale ZA, Ramos C, Faria P, Soares JP, Canizes B, Khodr HM. Ancillary services market clearing simulation: A comparison between deterministic and heuristic methods, in Power and Energy Society General Meeting, 2010 IEEE; 2010. p. 1–6. [224] Kiliccote S, Piette MA, Koch E, Hennage D. Utilizing Automated Demand Response in commercial buildings as non-spinning reserve product for ancillary services markets, in Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on; 2011. p. 4354–4360. [225] Ela E, Kirby B, Navid N, Smith JC. Effective ancillary services market designs on high wind power penetration systems, in Power and Energy Society General Meeting, 2012 IEEE; 2012. p. 1–.8. [226] Kiliccote S. Field Testing of Automated Demand Response for Integration of Renewable Resources in California’s Ancillary Services Market for Regulation Products, ed; 2013. [227] Martinez VJ, Rudnick H. Active participation of demand through a secondary ancillary services market in a smart grid environment. Smart Grid IEEE Trans 2013;4:1996–2005. [228] Yang G, Zheng Z, Rui B, Hecker L, Jie Y, Okullo J. Quantifying the benefits of energy and ancillary services market, in Power and Energy Society General Meeting (PES), 2013 IEEE; 2013. p. 1–5. [229] Moscetti F, Paoletti S, Vicino A. Analysis and models of electricity prices in the Italian ancillary services market, in Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2014 IEEE PES; 2014. p. 1–6. [230] Passarello G, Tina GM, Brunetto C. Modeling of Italian ancillary services market: Sicilian case study, in Clean Electrical Power (ICCEP), 2015 International Conference on; 2015. p. 370–375. [231] Saebi J, Taheri H, Mohammadi J, Nayer SS. Demand bidding/buyback modeling and its impact on market clearing price, in 2010 IEEE International Energy Conference; 2010. p. 791–796. [232] Samimi A, Nikzad M, Siano P. Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs. Renew Energy 2017;109:22–40.

Control programs, in 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton); 2013. p. 1029–1036. Battegay A, Hadj-Said N, Roupioz G, Lhote F, Chambris E, Boeda D, et al. Impacts of direct load control on reinforcement costs in distribution networks. Electr Power Syst Res 2015;120:70–9. Nur A, Kaygusuz A. Load control techniques in smart grids, in 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG); 2016. p. 1–4. Stenner K, Frederiks ER, Hobman EV, Cook S. Willingness to participate in direct load control: the role of consumer distrust. Appl Energy 2017;189:76–88. Stoft S. PJM's capacity market in a price-spike world. University of California Energy Institute; 2000. Cramton P, Stoft S. A capacity market that makes sense. Electr J 2005;18:43–54. Hobbs BF, Hu MC, Inon JG, Stoft SE, Bhavaraju MP. A dynamic analysis of a demand curve-based capacity market proposal: the PJM reliability pricing model. IEEE Trans Power Syst 2007;22:3–14. Sener AC, Kimball S. Reviewing progress in PJM's capacity market structure via the new reliability pricing model. Electr J 2007;20:40–53. Pfeifenberger J, Spees K, Schumacher A. A comparison of PJM’s RPM with alternative energy and capacity market designs, Prepared for PJM Interconnection, Inc., September; 2009. Cramton P, Ockenfels A, Stoft S. Capacity market fundamentals. Econ Energy Environ Policy 2013;2:27–46. Benalcazar P, Kamiński J. Capacity markets and cogeneration facilities: recommendations for Poland. Polit Energ 2016;19:61–76. Bhagwat PC, de Vries LJ, Hobbs BF. Expert survey on capacity markets in the US: lessons for the EU. Uti Policy 2016;38:11–7. Christiansen PN. Equilibrium modeling of a power market with a capacity market designed to promote flexible capacity. NTNU; 2016. Hach D, Chyong CK, Spinler S. Capacity market design options: a dynamic capacity investment model and a GB case study. Eur J Oper Res 2016;249:691–705. Hawker G, Bell K, Gill S. Capacity markets and the EU target model–a Great Britain case study; 2016. Long S, Liao Z. An investigation of bureaucratic influences on absorptive capacitymarket responsiveness relationships. Asian J Technol Innov 2016;24:142–58. Vanadzina E, Gore O. Capacity market in Russia: Possibilities for new generation entry and cost of CRMs, in 2016 13th International Conference on the European Energy Market (EEM); 2016. p. 1–5. Blumsack S, Yoo K, Johnson N. Can Capacity Markets Be Designed by Democracy?, in Proceedings of the 50th Hawaii International Conference on System Sciences; 2017. Liu Y. Demand response and energy efficiency in the capacity resource procurement: case studies of forward capacity markets in ISO New England, PJM and Great Britain. Energy Policy 2017;100:271–82. Yong F, Shaohua Z, Yuzeng L. An incentive compatiblecontract for interruptible load management in electricity market. Autom Electr Power Syst 2003;14:004. Fang Y, Li Y-Z. Modeling and implementation of incentive interruptible load contracts in electricity markets. Power Syst Technol 2004;17:009. Jianxue W, Xifan W, Xiaoying D. The Forward Contract Model of Interruptible Load in Power Market, in 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific; 2005. p. 1–5. Tuan LA, Bhattacharya K, Daalder J. Transmission congestion management in bilateral markets: an interruptible load auction solution. Electr Power Syst Res 2005;74:379–89. Wang J-X, Wang X-F, Wang X-l. Study on model of interruptible load contract in power market. Proc-Chin Soc Electr Eng 2005;25:11. Yu CW, Zhang S, Chung TS, Wong KP. Modelling and evaluation of interruptibleload programmes in electricity markets, IEE Proc - Gener Trans Distrib, 152; 2005. p. 581–588. Baldick R, Kolos S, Tompaidis S. Interruptible electricity contracts from an electricity retailer's point of view: valuation and optimal interruption. Oper Res 2006;54:627–42. Si W, Li Y. Modeling and Implementation of Incentive Interruptible Load Contracts in Electricity Markets, in 2010 Asia-Pacific Power and Energy Engineering Conference; 2010. p. 1–5. Yan L, Fang S, Gang S, SONG Y-W, WU Z-H, Pei Z-X, et al. Transmission capability of the grid cross section considering the interruptible load management. DEStech Trans Environ Energy Earth Sci 2016. Yang L, He M, Vittal V, Zhang J. Stochastic optimization-based economic dispatch and interruptible load management with increased wind penetration. IEEE Trans Smart Grid 2016;7:730–9. Piette MA, Watson D, Motegi N, Kiliccote S, Xu P. Automated Critical Peak Pricing Field Tests: Program Description and Results, ed; 2006. Herter K. Residential implementation of critical-peak pricing of electricity. Energy Policy 2007;35:2121–30. Herter K, McAuliffe P, Rosenfeld A. An exploratory analysis of California residential customer response to critical peak pricing of electricity. Energy 2007;32:25–34. Joo JY, Ahn SH, Yoon YT, Choi JW. Option Valuation Applied to Implementing Demand Response via Critical Peak Pricing, in 2007 IEEE Power Engineering Society General Meeting; 2007. p. 1–7. Piette MA, Watson D, Motegi N, Kiliccote S. Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results, ed; 2007. Wolak FA. Residential Customer Response to Real-time Pricing: The Anaheim Critical Peak Pricing Experiment, ed; 2007.

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