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A new demand response scheme for electricity retailers
Abstract
A new demand response (DR) scheme from the retailers’ point of view is presented in this paper. The proposed DR scheme allows a retailer to decide how to buy DR from aggregators and consumers. Various long-term and real-time DR agreements are proposed, where they are considered as energy resources of retailers in addition to the commonly used providers. These innovative agreements include pool-order options, spike-order options, forward DR contracts and reward-based DR. A stochastic energy procurement problem for retailers is formulated, in which pool prices and customers’ participation in the reward-based DR are uncertain variables. The feasibility of the problem is assessed using a realistic case of the Queensland jurisdiction within the Australian National Electricity Market (NEM). The outcomes indicate the usefulness of the given DR scheme for retailers, particularly for the conservative ones.
... Refs. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] are reviewed and compared in Table 2. According to Table 2, the objective function of these references is minimizing cost [14,15,[28][29][30], maximizing profit [16][17][18][19][20][21][22][23][24][25][26][27][28][31][32][33] or minimizing selling price [32,33]. ...
... [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] are reviewed and compared in Table 2. According to Table 2, the objective function of these references is minimizing cost [14,15,[28][29][30], maximizing profit [16][17][18][19][20][21][22][23][24][25][26][27][28][31][32][33] or minimizing selling price [32,33]. The types of considered selling price is no pricing [14,15,29,30], fixed pricing [16-24 ,26-28,31-33] and TOU pricing [25]. ...
... The types of considered selling price is no pricing [14,15,29,30], fixed pricing [16-24 ,26-28,31-33] and TOU pricing [25]. Also, the solution method is based on hybrid binary imperialist competitive algorithm-binary particle swarm optimization (BICA-BPSO) [14] or GAMS optimization package [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Furthermore the uncertainty model includes Monte Carlo simulation (MCS) [16], scenario based method [17][18][19][20][21][22][23][24][25][26][27][28]32,33], robust optimization approach (ROA) [29,30] and information gap decision theory (IGDT) [31]. ...
In this paper, bilateral contracting and selling price determination problems for an electricity retailer in the smart grid environment under uncertainties have been considered. Multiple energy procurement sources containing pool market (PM), bilateral contracts (BCs), distributed generation (DG) units, renewable energy sources (photovoltaic (PV) system and wind turbine (WT)) and energy storage system (ESS) as well as demand response program (DRP) as virtual generation unit are considered. The scenario-based stochastic framework is used for uncertainty modeling of pool market prices, client group demand and variable climate condition containing temperature, irradiation and wind speed. In the proposed model, the selling price is determined and compared by the retailer in the smart grid in three cases containing fixed pricing, time-of-use (TOU) pricing and real-time pricing (RTP). It is shown that the selling price determination based on RTP by the retailer leads to higher expected profit. Furthermore, demand response program (DRP) has been implemented to flatten the load profile to minimize the cost for end-user customers as well as increasing the retailer profit. To validate the proposed model, three case studies are used and the results are compared.
... Several DR program schemes have been proposed in the literature, representing the DR scheduling from the viewpoint of the electricity retailers, e.g., [1,2,8,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Compared to the DR setups that can be implemented by the transmission system operators (TSO) and the distribution companies (DISCO), this problem has been less explored from the perspective of retailers. ...
... The incentive-based DR programs that the retailers could offer to their customers are formulated in Refs. [1,8,20,[22][23][24]. Employing incentive-based DR programs stimulates the consumption with the rewards offered to the customers for demand reduction [25]. ...
... Refs. [1,20] ESSs owned by retailer r at bus b designing the DR programs. The DR scheme is considered as an energy source of the retailers. ...
In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.
... The research [20] has reviewed market demand-side strategies to analyze consumers' roles. In another study [21], Mahmoudi et al. analyzed long-term planning for retailer's cost minimization based on four DRPs: reward-based, pool-order, spikeorder, and forward DR. In this work the uncertainty of electricity market price was considered using the stochastic method. ...
... The related constraints to the storage are shown in Eq. (18) = b el P el m,d,t + c el (21) Cost el = b el P el m,d,t + c el (22) The cost function of the fuel cell is modeled by Eq. (23). It is shown that the cost function of the fuel cell is a quadratic function that causes nonlinearity in optimization programming. ...
Using renewable energy sources (RES) and green hydrogen has increased dramatically as one of the best solutions to global environmental issues. Applying demand response programs (DRPs) in this context could enhance the system’s efficiency. Evaluating different DRPs’ performances and assessing economic impacts on different parts of the electricity market is essential. The inherent uncertainty of RES and prices is inevitable in electricity markets. As a result of the lack of information, it is crucial to mitigate the risks as much as possible, such as risks related to changes in demand, unit outages, or other traders’ bid strategies. This research introduces a robust multi-objective optimization method to reach the most confident plan for the retailer based on uncertainty in RES and price. The integration of different DRPs is assessed according to the cost to retailers and benefits for consumers using a multi-objective model to survey the impacts of different parts’ decisions on each other. The trade-off among DRPs is considered in this model, and they are traded using a new model to illustrate the daily effect of these programs in monthly operations. This paper uses hydrogen storage (HS) integrated with PV as a distributed energy resource. As the Iranian electricity market has just been established, this research proposes a framework for decision-making in new electricity markets to join future smart energy systems. The mid-term pricing evaluates the system’s performance for more accurate monthly results. Also, the operation cost of the hydrogen storage is modeled to assess its performance in non-robust and robust scheduling. Mixed-integer linear programming (MILP) has been used to model this problem in GAMS. A developed linearizing method is considered with a controllable amount of errors to reduce the volume and time of the computation. Finally, the cost of consumers in non-robust and robust market planning in the presence of DRPs is reduced by 8.77 % and 9.66 %, respectively, and HS has a compelling performance in peak-shaving and load-shifting.
... The perspective of the REP, and the specific optimization problems they seek to solve, is well-suited to a discussion of demand response. DR offers REPs a way to hedge their financial risk [20]. Our paper contributes to the limited body of literature [14,20,22] surrounding REP modelling. ...
... DR offers REPs a way to hedge their financial risk [20]. Our paper contributes to the limited body of literature [14,20,22] surrounding REP modelling. The sector and method of load control are varied in the literature. ...
In the face of the considerable volatility in electricity market prices, retail electric providers (REPs) occupy the difficult position between the utility and the end-user. They purchase electricity either through long-term contracts, in the day-ahead or spot markets, or self-generate. This electricity is then sold to the end-user at a fixed price. Thus, REPs bear the financial burden of electricity price uncertainty. In a market where prices can increase by orders of magnitude in as little as 15 min, single spikes have driven some REPs to bankruptcy. To mitigate the negative effects of market volatility, REPs can employ demand response (DR), shifting consumers’ electricity load from periods of high prices to lower-priced time periods. While DR comes in many forms, we focus on thermostatically-controlled residential DR, made possible through internet-connected thermostats. The timing of residential heating and cooling load makes it a particularly financially advantageous target for DR. We present a dynamic programming (DP) formulation designed to optimally schedule DR events to maximize their savings for the REP. We also consider the effects of price uncertainty on the savings through the use of stochastic dynamic programming (SDP). Our case study examines the profitability of thermostatically-controlled DR, specifically of air-conditioning (A/C) load, in the Electricity Reliability Council of Texas (ERCOT). We find that optimal management of thermostatically-controlled DR can generate significant savings to the REP, potentially improving their profit margins as much as 25%. However, we also find that few events generate most savings, necessitating precise scheduling facilitated by our formulation. Electricity price uncertainty marginally reduces these expected savings, but optimally-scheduled DR still offers REPs an effective method of mitigating risk.
... A comprehensive framework for this pricing scheme can be complicated, where the operator has to consider the energy that it buys from the upstream grid, which might be uncertain, and then sell it to consumers considering the proposed pricing scheme in this paper. This leads to a complex mathematical optimization, which is usually solved through a typical stochastic programming approach [9,[22][23][24][25]. Please note that this comprehensive framework is not the focus of our current ...
... A comprehensive framework for this pricing scheme can be complicated, where the operator has to consider the energy that it buys from the upstream grid, which might be uncertain, and then sell it to consumers considering the proposed pricing scheme in this paper. This leads to a complex mathematical optimization, which is usually solved through a typical stochastic programming approach [9,[22][23][24][25]. Please note Energies 2018, 11, 1388 6 of 12 that this comprehensive framework is not the focus of our current work, where our main aim is to develop clustering techniques to facilitate various pricing schemes and options for microgrid operators. ...
Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.
... A model is presented to set price changes which encourage customers to shift their loads considering time-of-use tariffs in [16]. In [17], a reward-based demand response scheme is presented from the retailer point of view. A bi-level programming approach is presented to solve the medium-term decision-making problem faced by a power retailer in [18]. ...
... In electrolyzer mode, the power is consumed to produce hydrogen molar in off-peak periods to be stored in hydrogen tanks. The minimum and maximum limits of consumed power by the electrolyzer are provided in constraints (16) and (17). Also, the maximum limit of produced hydrogen molar by electrolyzer is presented in (18). ...
... In none of the references the smart grid technologies are not considered while in Refs. [5,16,17,20] demand response program has been considered. ...
... Furthermore, the available power of wind turbine [54] and photovoltaic system [55] in each time period and scenario can be calculated using Equations (17) and (18), respectively. ...
In this research, an approach for selling price determination by electricity retailer in smart grid has been proposed with considering uncertainty. Also, hydrogen storage system (HSS) containing electrolyser (EL), hydrogen storage tanks (HST) and fuel cell (FC) are used as energy storage system (ESS) in smart grid. Also, demand response program as virtual generation units is proposed to increase retailer profit. Furthermore, renewable energy sources, i.e., photovoltaic (PV) system and wind turbine (WT), as well as power market (PM), bilateral contracts (BCs) and distributed generation (DG) units are used as multiple energy procurement sources. All uncertain parameters are modelled using the scenario-based stochastic framework. In the proposed model, the selling price is determined based on real-time pricing (RTP) by the retailer and it is compared with time-of-use pricing (TOU) in the smart grid. The selling price determination based on RTP and demand response program implementation increases the expected profit of retailer. Four case studies are investigated to validate the proposed model.
... Paper [7] models a two-stage approach in which the DR aggregator schedules the thermal heating load based on the day-ahead prices, but carries out DR in the balancing market through encouraging consumers by bonus prices. DR trading approaches in electricity markets as well as bilateral contracts are proposed in [8][9][10][11], without modelling the bottom-level DR programs. A new model is proposed in [12] through which the DR aggregator sells the DR obtained from load shifting, load curtailment, onsite generation and storage in the energy market. ...
... As a realistic example, ENERNOC, a DR aggregator, has contracts with TransGrid, the Transmission Network Service Provider in NSW, Australia, to provide DR for them [30]. We should emphasize that retailers can also be DR buyers, especially when market prices spike, where they can save their cost by reducing their required demand through buying DR from the aggregator [9,31]. It should be noted that a retailer requires knowing from which consumers the DR aggregator procures its DR. ...
... Refs. [1] [20] have considered the uncertain behavior of customers in ...
... The DR scheme is considered as an energy source of the retailers. Ref. [20] develops a DR scheme, where the retailer is not involved in the technical aspects of the DR program and procures various DR agreements from aggregators or large consumers . Ref. [8] proposes a coupon incentive-based DR program to induce flexibility in the retail customers on a voluntary basis. ...
This paper proposes a new methodology for the evaluation of reliability in radial distribution networks through the identification of new investments in this kind of networks, in order to reduce the repair time and the failure rate, which leads to a reduction of the forced outage rate and, consequently, to an increase of reliability. The novelty of this research work consists in proposing an ac optimization model based on mixed-integer nonlinear programming that is developed considering the Pareto front technique, in order to achieve a reduction of repair times and of failure rates of the distribution network components, while minimizing the costs of that reduction, the power losses cost, the cost of the optimal capacitor location and size, and the maximization of reliability, which is in the form of minimization of nonsupplied energy cost. In order to estimate the outage parameters, a fuzzy set approach is used. The optimization model considers the distribution network technical constraints. A case study using a 33-bus distribution network is presented to illustrate the application of the proposed methodology.
... Ref. [16] proposed an optimization problem that minimizes procurement costs of an electricity retailer in order to control demand response (DR) usage due to the integration of intermittent resources of power generation. Also, a demand response scheme is presented in [17] that allow a retailer to decide how to buy DR from aggregators and consumers considering longterm and real-time DR agreements. Ref. [18] presented a game theoretical model for the participation of energy retailers in electricity markets with flexible demand and real-time consumer prices. ...
... The profit maximization problem was simulated based on Eq. (17), considering the forecasted price data. The expected optimal profit, R 0 , is equal to $ 2,528,400. ...
... aggregators) to bid DR capacity on behalf of retail customers who receive incentives in exchange for reducing demand [112,337]. Overview -This DR mechanism can be used by retailers to leverage consumers' demand flexibility in order to shape retailers' load [22,349,350], with the objective of increasing its profits [351]. The triggers of this mechanism are either dynamic price signals or incentives, where consumers' loads are shifted from on-peak to off-peak periods with cheaper prices or to certain periods as requested by the retailer. ...
This research aims at exploring how smart grid opportunities can be leveraged to ameliorate demand response practices for residential prosumer collectives, while meeting the needs of end-users and power grids.
Electricity has traditionally been generated in centralized plants then transmitted and distributed to end-users, but the increasing cost-effectiveness of micro-generation (e.g. solar photovoltaics) is resulting in the growth of more decentralized generation. The term “prosumers” is commonly used to refer to energy users (usually households) who engage in small-scale energy production. Of particular interest is the relatively new phenomenon of prosumer collectives, which typically involve interactions between small-scale decentralized generators to optimize their collective energy production and use through sharing, storing and/or trading energy. Drivers of collective prosumerism include sustaining community identity, optimizing energy demand and supply across multiple households, and gaining market power from collective action.
Managing power flows in grids integrating intermittent micro-generation (e.g. from solar photovoltaics and micro-wind turbines) presents a challenge for prosumer collectives as well as power grid operators. Smart grid technologies and capabilities provide opportunities for dynamic demand response, where flexible demand can be better matched with variable supply. Ideally, smart grid opportunities should incentivize prosumers to maximize their energy self-consumption from local supply while fairly sharing any income from trading surplus energy, or any loss of utility associated with altering energy demand patterns.
New businesses are emerging and developing various products and services around smart grid opportunities to cater for the socio-technical needs of residential prosumer collectives, where technical energy systems overlap with social interactions. This research studies how emerging businesses are using smart grid capabilities to create dynamic demand response solutions for residential prosumer collectives, and how fairness can be adopted in solutions targeting those collectives.
This research interweaves social and technical knowledge from literature to interpret the interactions and objectives of prosumer collectives in new ways, and create new socio-technical knowledge around those interpretations.
Conducting this research involved using mixed research methods to draw on social science, computer science, and power systems. In the social stream of the research, semi-structured interviews were conducted with executives in businesses providing current or potential smart grid solutions enabling dynamic demand response in residential prosumer collectives. In the technical stream, optimization, computation and game theory concepts were used to develop software algorithms for integrating fairness in allocating shared benefits and loss of utility in collective settings.
Interview findings show that new business models and prosumer-oriented solutions are being developed to support the growth of prosumer collectives. Solutions are becoming more software-based, and enabling more socially-conscious user choice. Challenges include dealing with power quality rather than capacity, developing scalable business models and adequate regulatory frameworks, and managing social risks. Automated flexibility management is anticipated to dominate dynamic demand response practices, while the grid is forecast to become one big prosumer community rather than pockets of closed communities. Additionally, the research has developed two software algorithms for residential collectives, to fairly distribute revenue and loss of utility among households. The algorithms used game theory, optimization and approximation algorithms to estimate fair shares with high accuracy using less computation time and memory resources than exact methods.
... In addition, Wijaya et al. (2014) suggest that DR programs can benefit both suppliers and residential customers. With regard to incentive-based DR programs, several field studies have examined their economic effects, such as benefits to electricity retailers (Mahmoudi et al., 2014) and demand flexibility and cost-effectiveness under coupon-based DR (Zhong et al., 2012). ...
Although numerous studies have examined the economic benefits of demand response programs, the environmental impacts of such programs have been relatively underexplored. This study assesses the impact of demand resource bidding on the wholesale energy market and the environment, based on three years of high temporal-resolution data from Korea. In this demand resource bidding program, successful bidders were paid the system marginal price for reducing their electrical load at a given hour, which in turn reduced the generation of power from various technologies. This investigation of how carbon dioxide and particulate matter emissions from existing power systems changed with the introduction of the demand bidding program finds that the program altered the system operator's electricity generation portfolio and marginally abated carbon dioxide and particulate matter emissions from the power sector. It also shows that the environmental impact of the program varied over the course of the day and the year. The modest but statistically significant environmental impact of the demand resource bidding program points to the importance of including electricity demand resources in the discussion and development of energy and environmental policies for the power sector.
... An overview of different DR programs and their theoretical background was provided by Ref. [6] and their presence in various countries was detailed by Ref. [7]. A lot of studies are being conducted in the field of DR in different categories, which include smart appliances [8], energy management systems [9][10][11], smart grids [12][13][14][15][16][17], load shifting [18], comfort optimization [19], power quality [20], demand aggregation [21], integration of RE [22][23][24], pricing [25][26][27][28][29][30], energy storage [31][32][33][34], contracts [35,36], risk management [33,[37][38][39] etc. ...
Owing to countless advancements in information technology, all residential consumers, in any part of the world, are empowered to contribute to demand response (DR) programs, to manage their electricity usage and to cut associated expenses using a suitable energy management system. Solutions based on consumer participation in meeting the growing electricity demand are carried out through different programs, and incentive-based demand response (IBDR) programs play an important role in these circumstances. However, introducing such programs to any new market needs consumers' willingness along with a good policy support. This study assesses the willingness and interest of consumers to participate in different IBDR programs and the associated need for developing policies, based on the consumers’ feedback, in a subsidized electricity market such as that of Kuwait. A survey was conducted to get feedback from consumers on three different IBDR programs and four incentive schemes.
After establishing the association between incentivization and load reduction, and identifying consumers’ choice on the most preferred IBDR programs and incentive schemes, the results were used to assess the need for different policy strategies for a typical subsidized market. The results of this study can be taken as a reference for formulating policies and programs for similar markets. The analysis on the impact of the programs indicates that by implementing IBDR programs, in addition to the financial benefit to both consumers and implementer, Kuwait can maintain its reserve capacity without any further addition of power plants.
... Incomplete information game theory is used to model the interaction of players in a variety of power markets. In (Mahmoudi et al., 2014b), authors have proposed a DR scheme from the retailers' point of view. The retailer decides how to deal DR from aggregators. ...
In recent years, significant development in smart metering and remote sensing systems in the electricity industry, especially on the side of consumers, it has made in implementation demand response programs in peak periods possible. The present study aims to present a game theoretical approach to the optimal bidding strategies for demand response (DR) aggregators in deregulated energy market. This model is based on the customer benefit function and price elasticity so that an economic responsive load model is applied to DR implementation. In this paper, the interaction between a system operator and aggregators in a deregulated market is modeled in this paper, where DR aggregator provides DR service to the system operator. It is assumed that a system operator collects bids from DR aggregators and determines each aggregator share in the demand response programs by maximizing its revenue function, and also, offers rewards to DR aggregators to reach this goal. On the other hand, DR aggregators compete together to offer their DR services to the network operator and in this way provide compensation for customers. The competition between DR aggregator participants is modeled as a non-cooperative game considering incomplete information. This game is solved using the Nash equilibrium idea. By the implementing the proposed method, the operator's profit rises up 7 percent.
... Among them, literature [1] used psychology to build user's selection model for different electric selling companies. Literature [2] designed a variety of user response mechanisms based on price. Literature [3] designed a time-sharing tariff strategy for users through simulation. ...
With the liberalization of China’s electricity sales side, a large number of power sales companies emerge and occupy the market, forming a pattern that many power sales companies are competing for the market. Based on the analysis of the connotation, type and purchasing and selling electricity flow of the power selling company, this paper first proposes the power user response model based on peak and valley time-sharing electricity price, then constructs the purchasing and selling electricity model of the power selling company considering differentiated time-sharing electricity price, and finally analyzes the purchasing and selling electricity model through the example. The results show that the model can optimize the load curve of power users and reduce the cost of electricity while maximizing the profit of power selling companies.
... Another DR model is presented in [70], where electricity retailers are given an opportunity to procure their DR through long-term bilateral contracts to short-term DR. Various DR contracts such as forward DR and DR options are developed. ...
One of the key features of future power networks, referred to as smart grids, is deploying demand-side resources in order to reduce the stress at the supply side. This implies active participation of electricity customers, as a societal network, in the power networks, as a physical network, which increases the interdependencies of these two networks due to the effect of demand response programs on power systems. Furthermore, in the future smart cities there is a crucial need to take advantage of demand-side resources to supply electricity in a sustainable manner. In this context, demand response programs play a pivotal role in electricity market in order to achieve supply-demand balance by taking advantage of the load flexibility.
... Also, a model is provided to determine retail price while customers participate in demand response for flat- tening load profile in García-Bertrand (2013). In Mahmoudi et al. (2014b), a new option of demand response program based on reward- based strategy is used by a retailer. A mid-term scheduling of retailer is addressed using a bi-level approach in Carrión et al. (2009). ...
This paper proposes optimal management of hydrogen storage systems and plug-in electric vehicles in the scheduling of retailer under pool market price uncertainty which real-time pricing is determined in comparison with time-of-use pricing and fixed pricing. Also, pool market price uncertainty is modeled via proposed interval optimization approach for uncertainty-based profit function of retailer. In the proposed model, uncertainty-based profit function of retailer is reformulated as a deterministic bi-objective framework with average and deviation profits as the conflict objective functions which deviation profit should be minimized while average profit should be maximized. Furthermore, weighted sum approach is used to solve the proposed bi-objective model in order to obtain Pareto solutions. Finally, fuzzy decision-making approach is provided to select the trade-off solution from Pareto solutions. The proposed MIP-based model is implemented in GAMS software which can be solved using CPLEX solver. Deterministic and interval optimization approaches under fixed, time-of-use, and real-time pricing are utilized in the case studies and the results are compared with each other in order to show the effectiveness of the proposed model. The obtained results show that deviation profit of retailer decreases in the proposed interval optimization approach in comparison with deterministic approach. Also, average profit of retailer increases in the real-time pricing in comparison with time-of-use pricing and fixed pricing.
... Also, a model is provided to determine retail price while customers participates in demand response for flatten load profile in [31]. In [32], a new option of demand response program based on reward-based is used by retailer. A mid-term scheduling of retailer is addressed using a bi-level approach in [33]. ...
In the smart grid, retail price determination by electricity retailer is necessary in the presence of hydrogen storage systems and plug-in electric vehicles under pool market price uncertainty. Therefore, in this paper, real-time pricing is proposed in comparison with time-of-use pricing and fixed pricing. Furthermore, an interval optimization approach is proposed for pool market price uncertainty modeling. In this approach, uncertainty-based profit function of retailer is reformulated as a deterministic bi-objective problem with average and deviation profits as the conflict objective functions which average profit should be maximized while deviation profit should be minimized. Furthermore, epsilon constraint method is used to solve the proposed bi-objective model in order to obtain Pareto solutions. Finally, fuzzy satisfying approach is used to select the trade-off solution from Pareto solutions. The obtained results show that average profit of retailer increases in the real-time pricing in comparison with time-of-use pricing and fixed pricing. Also, deviation profit of retailer decreases in the proposed interval optimization approach in comparison with deterministic approach. The proposed mixed-integer linear programming model is solved using CPLEX solver under GAMS optimization software. To validate the better performance of proposed model, three types of retail price determination under deterministic and interval optimization approaches are utilized and the results are compared with each other.
... Different contracts and agreements that can be used by retailers to buy DR from aggregators and end users were proposed by [143] with the help of a newly developed DR scheme. The results claim that due to the increasing usage of DR options in summer, the percentage use of DR is higher in summer than in winter. ...
Since the export of fossil fuel is the backbone of Kuwait's economy, the enormous upsurge in the internal fuel consumption creates anguish and bane. At present, 46% of the internal fossil fuel consumption is used for electricity generation. The increase in fuel consumption hurled Kuwait as one of the highest per capita electricity consuming as well as CO2 emitting countries in the world. Consumers pay only 6% of the production cost of electricity—the government subsidizes the balance—, which is one of the major reasons behind the high electricity consumption in the country. Any reduction in electricity consumption can positively reflect on both per capita electricity consumption and CO2 emission. Additionally, the saved fossil fuel can be exported to generate revenue to support the country's economy. Worldwide, demand side management (DSM) is considered as an effective tool to curtail electricity demand. The objective of this review paper is to explore the available literature to find out the most suitable DSM measures to control the growth of per capita electricity consumption in Kuwait. After conducting a detailed strengths, weaknesses, opportunities and threats (SWOT) analysis, it is concluded that incentive-based demand response programs can be considered as one of the most suitable solutions to address this problem.
... Ref. [16] proposed an optimization problem that minimizes procurement costs of an electricity retailer in order to control demand response (DR) usage due to the integration of intermittent resources of power generation. Also, a demand response scheme is presented in [17] that allow a retailer to decide how to buy DR from aggregators and consumers considering long-term and realtime DR agreements. Ref. [18] presented a game theoretical model for the participation of energy retailers in electricity markets with flexible demand and real-time consumer prices. ...
In restructured electricity markets, the electricity retailers have various electricity acquisition strategies to supply consumer electricity demand from alternative resources such as distributed generations (DGs), bilateral contracts and pool market purchase considering demand response programs (DRP). This paper applies a particle swarm optimization (PSO) algorithm to find electricity acquisition for electricity retailers with multiple acquisition options. Also, this paper is focused to study the effect of DRP on total acquisition cost, where the time-of-use (TOU) rates DRP have been modeled and consequently its influence on load profile and acquisition cost reduction has been discussed. A case study is used to illustrate the efficiency of the proposed algorithm.
... Ref. [16] proposed an optimization problem that minimizes procurement costs of an electricity retailer in order to control demand response (DR) usage due to the integration of intermittent resources of power generation. Also, a demand response scheme is presented in [17] that allow a retailer to decide how to buy DR from aggregators and consumers considering long-term and real-time DR agreements. Ref. [18] presented a game theoretical model for the participation of energy retailers in electricity markets with flexible demand and real-time consumer prices. ...
In restructured electricity markets, the electricity retailers have various electricity acquisition strategies to supply consumer electricity demand from alternative resources such as distributed generations (DGs), bilateral contracts and pool market purchase considering smart grid technology. Demand response programs (DRP) is used as smart grid technology. This paper applies an imperialist competitive algorithm (ICA) to find electricity acquisition for electricity retailers with multiple acquisition options under DRP. Also, this paper is focused to study the effect of DRP on total acquisition cost, where the time-of-use (TOU) rates DRP have been modeled and consequently its influence on load profile and acquisition cost reduction has been discussed. A case study is used to illustrate the efficiency of the proposed algorithm.
... Although some reports have been addressed in the literature regarding the demand-side players who bid in electricity markets [4], [5], [9], DR aggregation has not been studied in these reports. ...
Participation of consumers in Demand Response (DR) programs improves system stability and reliability as well as market efficiency. Retailers and distributors purchase DR to advance business and system reliability, respectively. Meanwhile, large consumers, Distribution System Operators (DSOs), Load Service Entities (LSEs), and DR aggregators sell DR to increase their own profits. In this context, DR aggregators are key elements of power systems that enhance the participation of consumers in electricity markets. These market participants can negotiate their aggregated DR with other market players in Demand Response eXchange (DRX) markets, and participate in the energy and ancillary service markets. Hence, this paper proposes a stochastic model to optimize the performance of a DR aggregator to take part in the day-ahead energy, ancillary services and intraday DRX markets. In order to mitigate the negative impacts of uncertainties, Conditional Value at Risk (CVaR) is also incorporated to the proposed model. Numerical studies indicate that the proposed model for DR aggregator can arrange its offering/bidding strategies to participate in the mentioned markets simultaneously.
... They applied publicly available expenditure data and utility-level consumption data from several major U.S. cities, complementing studies that implemented individual billing data. Mahmoudi et al. (2014) presented a new demand response (DR) scheme from the retailers' point of view. ...
This paper presents a study to determine demand for electricity in city of Yazd, Iran over the period of 1998-2008. Using vector error correction model (VECM) based on seasonal information, the study determines that electricity has no elasticity in short term in household expenditure. Therefore, government policy on increasing price of electricity will not influence demand. However, electricity maintains elasticity over the long-term period and an increase on price of electricity could motivate consumers to reduce their consumption by purchasing energy efficient facilities. Therefore, any governmental strategy to increase price may have positive impact on economy. The study also detects a positive and meaningful relationship between temperature and electricity consumption.
An electricity retailer, as a profit-oriented company, is an intermediary between large producers and end consumers of electricity. The smart grid structure provides retailers with facilities such as telecommunications infrastructure, energy management systems, distributed generation resources, and energy storage systems to meet the needs of end consumers. Therefore, energy procurement of retailer in smart grid environment is a big problem and challenge. In a way, it is possible to use the capacities of other retailers to achieve the goals of each retailer in such an environment. In this paper, the structure of the network is considered, with several retailers present in the smart grid environment. Therefore, a model has been proposed in which each retailer in his area to settle energy and also in accordance with a cooperative game with other retailers, to reach an agreement with them on the amount of exchangeable power and its price. A distributed method is used to day-ahead energy procurement of retailers. Each retailer clears energy in their area by solving a Mixed Integer Linear Programing (MILP). The Mayfly optimization (MO) algorithm is used to find the best energy exchange price between retailers. To evaluate the efficiency of the proposed method, a network with the presence of three retailers has been studied. Also, 4 different case studies have been used to compare the proposed method with the state without energy exchange between retailers and also to investigate the effect of the presence of renewable energy resources and energy storage system on the profitability of retailers. In the absence of distributed generation resources and energy storage system, the use of the proposed method compared to the method of non-exchange of energy between retailers has increased the total profit of retailers by 2.1 %. This rate of profit increase using the proposed method in the presence of distributed generation sources and energy storage system was 7.8 %. Additionally, to evaluate the efficiency of using MO in the method proposed in this paper, this algorithm has compared with Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) algorithms. Also in this paper, the effect of considering the uncertainty of the pool market price in the proposed method has investigated. In this paper, the effect of parameters of storage systems and wind power generation on retailers' profits has evaluated.
The continued development of distributed energy resources (DER), information and communications technologies is enabling a greater number of parties to participate in electricity markets. This review explores the various methods that DERs may utilize to participate in electricity markets, the differing architectures and methodologies used to model their participation, the past and future development of communications technologies that has enabled this participation, and examples of commercial implementations of solutions to DER participation in electricity markets.
In September 2020, China has made a commitment to achieving “peak carbon dioxide emissions” before 2030 and “carbon neutrality” before 2060 at the General Assembly of the United Nations. Then, at the Climate Ambition Summit, China pointed out the important responsibility and mission of the power industry to serve "peak carbon dioxide emissions and carbon neutrality". The key issues such as what kind of electric market to build and what kind of main energy will be chosen to build the power system were proposed. For the new power system, there will be new changes in the links of “source, network, load, storage”. However, the reform of the electricity market cannot be ignored, either. How to determine the type of electricity sales company and how to develop a suitable electricity sales strategy is one of the important studies that need to be explored at present. Therefore, the main body of electricity sales companies under new power system reform in China are analyzed in our studies, Then, we reviewed the current situation of electricity market reform at home and abroad. Finally, the thought on electricity market reform is presented.
In the electricity markets, retailers purchase the required demand of their consumers from different energy resources such as self-generating facilities, bilateral contracts, and pool markets. The plug-in electric vehicles and hydrogen storage systems and fuel cells can bring various flexibilities to the energy management problem. In this paper, the selling price determination and energy management problem of an electricity retailer in the smart grid under uncertainties has been proposed. Multiple energy procurement sources containing pool market, bilateral contracts, distributed generation units, renewable energy sources (photovoltaic system and wind turbine), plug-in electric vehicles, and hydrogen storage systems are considered. The scenario-based stochastic method is used for uncertainty modeling of pool market prices, consumer demand, temperature, irradiation, and wind speed. In the proposed model, the selling price is determined and compared in three cases containing fixed pricing, time-of-use pricing, and real-time pricing. It is shown that the selling price determination based on real-time pricing and flexibilities of plug-in electric vehicles and hydrogen storage systems leads to higher expected profit. The proposed model is formulated and solved using the invasive weed optimization(IWO) algorithm. To validate the proposed model, three types of selling price determination under four case studies are utilized and the results are compared.
The sales aspect of electricity reform is one of the focuses of current reform in China. Independent retailers are involved in the purchase of electricity, which is a sign of a mature electricity market. With the increasing degree of market opening, it is expected to become an important link between the generation side and demand side, and its degree of participation will directly affect the benefits of the demand response. From the analysis of the operation pattern of future electricity retailers that are involved in optimal generation dispatching, the load-calculation model of the customer response and the dispatching model of electricity retailers involved in generation are established. Then, based on the principle of dynamics, a comprehensive assessment model is established. The results show that customers’ response to electricity retailers involved in the generation dispatch can improve the load rate by about 4%. The overall efficiency of various stakeholders is positive, and the investment cost of the government environmental management is gradually decreased.
Enduring expansions and advancement in the knowledge driven energy-economy era promise a power system that is cost-effectively competent, environmentally responsive, flawless and operationally acquiescent. This imminent power system will depend on latest communication technologies, computational easiness, and monitoring and control strategies attributable to the consumers or end users. Amid the numerous advances related to this development, a strategic and significant component is demand side management (DSM). Demand response can play an important and pertinent role in the milieu of DSM, and incentive-based demand response program (IBDRP) is getting customer focus in many parts of the world. This study tries to bring out major aspects to be considered while introducing IBDRP in retail electricity markets. Document analysis, a well-known qualitative research methodology, is used for analyzing various IBDRP schemes practiced globally, with the help of the ATLAS.ti software and utilizing the snowball sampling technique. For better understanding, the results of the axial coding are arranged in three stages: pre-implementation stage, implementation stage, and post-implementation stage. Based on the importance and their frequency of appearance, ten most prominent codes were identified, and the strength of their interrelationships is presented on a multilevel scale with three different ranks. One of the major and important outcomes of this research is that, with the help of the proposed conceptual framework, a proper implementation framework can be built, which can be used for developing most appropriate IBDRP for any retail electricity market.
Severe environmental problems are resulted from the rapid growth of electricity consumption in China, since thermal power plant occupies the predominant position in power generation and releases large amount of pollutants and greenhouse gas. Electricity consumption of residential sector grows even faster than that of industrial sector in recent years. Residential sector is increasingly believed to have huge potential of energy saving and efficiency improvement. This paper aims at discovering the regional difference of household electricity consumption (HEC). First, a research framework is provided to identify the characteristics of HEC from the following three dimensions, consumption volatility, pattern similarity and temperature sensitivity. Households are divided into different groups in each city. The major household groups that account for the largest proportion are identified and then regarded as representatives of different cities. The fluctuation characteristics of their typical load profiles are compared and discussed in detail. Moreover, we design an Empty City Index (ECI) to further discuss the relationship between HEC and the population flow in special time intervals, for example, Chinese Spring Festival. The empirical study demonstrates that population outflow during Spring Festival is more obvious in wealthier city. Through k-means clustering, typical household groups as well as their typical load profiles are identified in each city. And the results show that under the current tiered pricing for household electricity (TPHE), nearly 10% households in each city still have potential for load reduction and load shifting. For the entire Jiangsu province, such households account for 12.5%. Policy implications and the applicability of this study are suggested and discussed respectively to government, household customers and electric power companies.
This paper investigates a bi-level stochastic energy acquisition strategy for electricity retailers with self-generation facilities to supply clients' demand from various resources of energy. The main propose of the presented work is modeling clients' switching behavior based on uncertainties of rival retailers' strategy and wholesale prices. The upper sub-problem models wholesale price fluctuations and determines retailer's participation levels in the forward and wholesale markets as well as scheduling of self-generation facilities based on the minimum supply cost. In the lower sub-problem, the Dempster-Shafer evidence theory (DSET) is applied to determine the retail selling price based on rivals' strategies and clients' switching tendency. DSET defines two belief and plausibility functions to evaluate the possibility of accepting selling prices by clients. According to the retailer's defined belief level and the minimum expected cost, the selling price is calculated in the lower sub-problems. Finally, a case study is used to show the performance of proposed method.
This paper introduces a model that integrates a Unit Commitment (UC) model, which performs the simulation of the day-ahead electricity market, combined with an econometric model that estimates the income and price elasticities of electricity demand. The integrated model is further extended to estimate the retailers’ profitability with demand responsive consumers. The applicability of the proposed model is illustrated in the Greek day-ahead electricity market. The model is designed to identify the effects of demand responsiveness to the fluctuations of spot prices, based on their short-term price elasticities. It provides price signals on the profitability of retailers/demand aggregators, when forming their tariffs. We argue that the non-linearity between demand response and evolution of wholesale price, inherits risk for retailers. This finding could lead even to losses for some time periods, affecting strongly their viability. The model provides useful insights into the risk of retailers from their price responsive customers and therefore acts as a pivotal study to policy makers and government officials (i.e. regulators, transmission and distribution system operators) active in the electricity market.
A system dynamics model of the electric demand response profit linkage mechanism is established based on the relationship of profit cycle among power generators, power grid companies and customers to address the problem of asymmetric distribution of demand response profits. The simulation system divided the demand response profit cycle chains into customer subsystem, power generator subsystem and power grid subsystem. This paper dissects the relationship among the internal and external factors of each subsystem by modeling. Taking the data of a city in China as foundation, the paper analyzes effects of the demand response plan on power generators and power grid companies under different distribution ratios of demand response profits. The results show that a reasonable distribution ratio can bring considerable economic benefits for the related bodies and can also enhance their enthusiasm to participate in the project which will ultimately generate huge benefits for the whole power system.
In future smart grids, the electricity suppliers can modify the customers' load consumption pattern by implementing appropriate DSM (demand side management) programs using smart meters. Most of the existing studies on DSM, only consider one utility company in the supplier side. In this paper, the possibility of existing more than one supplier in the smart grid is addressed by modeling the DSM problem as two non-cooperative games: the supplier side game, and the customer side game. In the first game, the suppliers' profit maximization problem is formulated by applying supply function bidding mechanism. In the proposed mechanism, the electricity suppliers submit their bids to the DSM center, where the electricity price is computed and is sent to the customers. In the second game, the customers aim to determine optimal load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are explored and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulations are performed for a smart grid system with 3 suppliers and 1000 customers. Simulation results demonstrate the superior performance of the proposed mechanism in reducing the peak load and increasing the suppliers' profit and the customers' payoff.
This paper presents the formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as small/medium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and/or price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the proposed scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs. Numerical illustration is performed based on realistic supply and demand data obtained from the Electric Reliability Council of Texas (ERCOT).
This paper sets forth a novel intelligent residential air-conditioning (A/C) system controller that has smart grid functionality. The qualifier “intelligent” means the A/C system has advanced computational capabilities and uses an array of environmental and occupancy parameters in order to provide optimal intertemporal comfort/cost trade-offs for the resident, conditional on anticipated retail energy prices. The term “smart-grid functionality” means that retail energy prices can depend on wholesale energy prices. Simulation studies are used to demonstrate the capabilities of the proposed A/C system controller.
It is widely agreed that an increased participation of the demand side in the electricity markets would produce benefits not only for the individual consumers but also for the market as a whole. This paper proposes a method for quantifying rigorously the effect that such an increase would have on the various categories of market participants. A new centralized complex-bid market-clearing mechanism has been devised to take into consideration the load shifting behavior of consumers who do submit price-sensitive bids. The effects of the proportion of demand response on the market are illustrated using a test system with ten generating units scheduled over 24 periods.
A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.
We consider interruptible electricity contracts issued by an electricity retailer that allow for interruptions to electric service in exchange for either an overall reduction in the price of electricity delivered or for financial compensation at the time of interruption. We provide a structural model to determine electricity prices based on stochastic models of supply and demand. We use stochastic dynamic programming to value interruptible contracts from the point of view of an electricity retailer, and describe the optimal interruption strategy. We also demonstrate that structural models can be used to value contracts in competitive markets. Our numerical results indicate that, in a deregulated market, interruptible contracts can help alleviate supply problems associated with spikes of price and demand and that competition between retailers results in lower value and less frequent interruption. Subject classifications: natural resources: energy, interruptible electricity; dynamic programming/optimal control. Area of review: Environment, Energy, and Natural Resources. History: Received December 2003; revision received June 2004; accepted June 2005.
This paper presents the optimization and implementation of a relaxed dynamic programming (RDP) algorithm to generate a daily control scheduling for optimal or near-optimal air conditioner loads (ACLs). The conventional control mode for ACL includes demand control, cycling control, and timer control, to assist customers for saving electricity costs. The proposed load control scheduler (LCS) scheme supports any combination of these three control types to save costs optimally during the dispatch period. Microprocessor hardware techniques were applied to carry out the proposed strategy for realistic application. The Visual C++ language was adopted as the developing tool to carry out the proposed work. Field tests of controlling air conditioners located in the campus of National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, were tested on-site to demonstrate the effectiveness of the proposed load control strategy. The results show that interruptible load scheduling can reduce the system load effectively, and the load capacity reduced by the proposed load control strategy follows closely the trajectory of the peak load.
The smart grid enables two-way energy demand response capability through which utility service providers can offer customers special call options for energy load curtailment. If a utility customer has a capability to perform a rapid cost and benefit analysis of the offer in an optimal manner and accept the offer, the customer can earn both an option premium to participate, and a strike price for load curtailments when asked. However, today most industrial customers still lack the ability to perform such an optimal and rapid cost and benefit analysis. This paper proposes a stochastic-programming enabled decision process to be used to evaluate the impact of load curtailments within a required short response time. This approach can build agility into utility customers' energy decisions, thereby helping exploit the energy demand response capability and achieve strategic advantage over competitors. An illustrative example of the proposed decision process under a call-option based energy demand response scenario is presented.
This paper deals with short-term decisions made by electricity retailers. It is assumed that a retailer aims to minimize the cost of procuring energy from two sources: one is the commonly-used pool market, and the other is the demand response (DR) program proposed in this paper. A reward-based DR is mathematically formulated where the volume of load reduction is modeled as a stepwise function of offered incentives by the retailer. Furthermore, a novel scenario-based participation factor is developed here to take into account the unpredictable behavior of customers. The presented problem is formulated in stochastic programming where its feasibility is evaluated on a realistic case of the Queensland region within the Australian National Electricity Market (NEM). Additionally, we define four distinct cases to study the impact of uncertainties associated with both resources, particularly DR, on short-term decisions of the retailer.
In order to support the growing interest in demand response (DR) modeling and analysis, there is a need for physical-based residential load models. The objective of this paper is to present the development of such load models at the appliance level. These include conventional controllable loads, i.e., space cooling/space heating, water heater, clothes dryer and electric vehicle. Validation of the appliance-level load models is carried out by comparing the models' output with the real electricity consumption data for the associated appliances. The appliance-level load models are aggregated to generate load profiles for a distribution circuit, which are validated against the load profiles of an actual distribution circuit. The DR-sensitive load models can be used to study changes in electricity consumption both at the household and the distribution circuit levels, given a set of customer behaviors and/or signals from a utility.
In this paper, we propose variational inequality models for electricity markets with time-of-use (TOU) pricing. Demand response is dynamic in the model through a dependence on the lagged demand. Different market structures are examined within this context. With an illustrative example, the welfare gains/losses are analyzed after an implementation of TOU pricing scheme over the single pricing scheme. It is intended that the proposed models would be useful: 1) for regulatory bodies in jurisdictions to assess market power; 2) to forecast future TOU prices; and 3) to examine welfare changes in electricity markets that change to TOU pricing from single pricing.
123 Abstract -- Several demand response (DR) programs, such as critical peak pricing (CPP), stipulate that electric utilities may schedule events only a limited number of times per year. Utilities must therefore use these events judiciously in order to maximize their benefits from the DR program. Traditionally, they have used rather simple decision rules to schedule such events (e.g., when temperature exceeds a certain threshold). In this paper, we present an option value approach for determining when to invoke these events. This approach calculates a dynamic threshold value, which represents the option value of deferring the use of one the events for use at any future point in the planning horizon (typically a year or cooling season). This threshold enables utilities to make the decision on whether or not to call an event based on the expected current benefits versus potential future benefits. This paper presents an example based on an actual DR program, which uses a simple, temperature based threshold, and compares it with our option based approach. Our approach can also be applied to any other criteria besides temperature such as reserve margins and generation cost.
This paper investigates the potential of providing aggregated intra-hour load balancing services using heating, ventilating, and air-conditioning (HVAC) systems. A direct-load control algorithm is presented. A temperature-priority-list method is used to dispatch the HVAC loads optimally to maintain consumer-desired indoor temperatures and load diversity. Realistic intra-hour load balancing signals were used to evaluate the operational characteristics of the HVAC load under different outdoor temperature profiles and different indoor temperature settings. The number of HVAC units needed is also investigated. Modeling results suggest that the number of HVACs needed to provide a {+-}1-MW load balancing service 24 hours a day varies significantly with baseline settings, high and low temperature settings, and the outdoor temperatures. The results demonstrate that the intra-hour load balancing service provided by HVAC loads meet the performance requirements and can become a major source of revenue for load-serving entities where the smart grid infrastructure enables direct load control over the HAVC loads.
One of the responsibilities of power market regulator is setting rules for selecting and prioritizing demand response (DR) programs. There are many different alternatives of DR programs for improving load profile characteristics and achieving customers’ satisfaction. Regulator should find the optimal solution which reflects the perspectives of each DR stakeholder. Multi Attribute Decision Making (MADM) is a proper method for handling such optimization problems. In this paper, an extended responsive load economic model is developed. The model is based on price elasticity and customer benefit function. Prioritizing of DR programs can be realized by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Considerations of ISO/utility/customer regarding the weighting of attributes are encountered by entropy method. An Analytical Hierarchy Process (AHP) is used for selecting the most effective DR program. Numerical studies are conducted on the load curve of the Iranian power grid in 2007.
The purpose of this paper is to analyze three formulations developed to facilitate participation of demand response resource Type-1 in the Midwest ISO's co-optimized energy and ancillary service market. While these three formulations appear similar on the surface, careful analysis will show that they can have different impacts on clearing and pricing outcomes. Based on this analysis, the formulation that can maintain reserve product priority and reserve clearing price order is selected and implemented in the Midwest ISO Security Constrained Economic Dispatch (SCED) and Security Constrained Unit Commitment (SCUC) market clearing processes. A 5-bus system is used to illustrate the features of each approach. This paper focuses on SCED formulations. The impact from the discussed formulations on SCUC should be similar.
This paper presents the design and evaluation of an effective market-clearing scheme for trading demand response (DR) in a deregulated power system. The proposed scheme is called demand response exchange (DRX), in which DR is treated as a public good to be exchanged between two groups of participating agents, namely DR buyers and DR sellers. While buyers require DR and are willing to pay for it, sellers have the capacity to curtail customer loads to supply DR on request. The DRX market clearing scheme uses Walrasian auctions, where agents update their DR quantity bids in response to prices adjusted by the market operator. This auction is repeated iteratively until market equilibrium is obtained at the point where the market outcome is proven to be Pareto optimal. The proposed scheme is tested on a small power system and its effectiveness substantiated.
A new approach to optimizing or hedging a portfolio of financial instruments to reduce risk is presented and tested on applications. It focuses on minimizing Conditional Value-at-Risk (CVaR) rather than minimizing Value-at-Risk (VaR), but portfolios with low CVaR necessarily have low VaR as well. CVaR, also called Mean Excess Loss, Mean Shortfall, or Tail VaR, is anyway considered to be a more consistent measure of risk than VaR. Central to the new approach is a technique for portfolio optimization which calculates VaR and optimizes CVaR simultaneously. This technique is suitable for use by investment companies, brokerage firms, mutual funds, and any business that evaluates risks. It can be combined with analytical or scenario-based methods to optimize portfolios with large numbers of instruments, in which case the calculations often come down to linear programming or nonsmooth programming. The methodology can be applied also to the optimization of percentiles in contexts outside of finance.
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The purpose of this paper is to examine the economic and technical perspectives of critical peak pricing plan as an active demand response(DR) program. To implement a good DR program, there are three perspectives to be considered: regulatory, economic, and technical perspectives. This paper will assume that the regulatory perspective of DR is determined as critical peak pricing(CPP) plan and examine the other two. The economic perspective of CPP plan is the incentive of the plan conductor, or the profit of an energy service provider(ESP). The technical perspective is a method to maximize the incentive of CPP plan, or an ESP's profit. An ESP should decide when to call critical peaks within certain constraints to maximize her profit. This is done by predicting the market prices and following a similar method as evaluating a swing option. The numerical example will show the optimal critical peak decisions.
A generic operations framework for a distribution company (disco) operating in a competitive electricity market environment is presented in this paper. The operations framework is a two-stage hierarchical model in which the first deals with disco's activities in the day-ahead stage, the day ahead operations model (DAOM). The second deals with disco's activities in real-time and is termed real-time operations model (RTOM). The DAOM determines the disco's operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco's actions and decision-making in this context.
This paper proposes a decision-making framework, based on stochastic programming, for a retailer: 1) to determine the sale price of electricity to the customers based on time-of-use (TOU) rates, and 2) to manage a portfolio of different contracts in order to procure its demand and to hedge against risks, within a medium-term period. Supply sources include the pool, self-production facilities and several instruments such as forward contracts, call options, and interruptible contracts. The objective is to maximize the profit and simultaneously to minimize the risks in terms of a multi-period risk measure. Moreover, the risks are measured using conditional value at risk (CVaR) methodology. The reaction of the customers to the retailers' selling prices as well as the competition between the retailers is modeled through a market share function. The problem is formulated as a mixed-integer stochastic programming. It is solved by a decomposition technique, and the decomposed parts are solved by a branch-and-bound algorithm.
In restructured power systems, there are many independent players who benefit from demand response (DR). These include the transmission system operator (TSO), distributors, retailers, and aggregators. This paper proposes a new concept-demand response eXchange (DRX)-in which DR is treated as a public good to be exchanged between DR buyers and sellers. Buyers need DR to improve the reliability of their own electricity-dependent businesses and systems. Sellers have the capacity to significantly modify electricity demand on request. Microeconomic theory is applied to model the DRX in the form of a pool-based market. In this market, a DRX operator (DRXO) collects DR bids and offers from the buyers and sellers, respectively. It then clears the market by maximizing the total market benefit subject to certain constraints including: demand-supply balance, and assurance contracts related to individual buyer contributions for DR. The DRX model is also tested on a small power system, and its efficiency is reported.
Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy-consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. Price uncertainty is modeled through robust optimization techniques. The model materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business. A simple bidirectional communication device between the power supplier and the consumer enables the implementation of the proposed model. Numerical simulations illustrating the interest of the proposed model are provided.
We explore three voluntary service options—real-time pricing, time-of-use pricing, and curtailable/interruptible service—that a local distribution company might offer its customers in order to encourage them to alter their electricity usage in response to changes in the electricity-spot-market price. These options are simple and practical, and make minimal information demands. We show that each of the options is Pareto-superior ex ante, in that it benefits both the participants and the company offering it, while not affecting the non-participants. The options are shown to be Pareto-superior ex post as well, except under certain exceptional circumstances.
As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.
This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.
In deregulated power systems, a load serving entity purchases electric energy from the wholesale market and sells it to its customers at regulated fixed prices. The load serving entity faces several uncertainties in its trading. This paper addresses the hedging problem of a load serving entity with the aid of interruptible load programs. A method, based on stochastic programming, is proposed to determine the optimal procurement of interruptible loads for a specified period of time. The objective is to minimize the market risks presented by a multi-period risk measure. Meanwhile, the conditional value at risk approach is used to measure the risks. In addition, different types of interruptible contracts are considered and their effects on the optimal procurement policy are analyzed. A case study is illustrated to demonstrate the proposed method.
Summary form only given. This paper develops a state queueing model to analyze the price response of aggregated loads consisting of thermostatically controlled appliances (TCAs). Assuming a perfectly diversified bad before the price response, we show that TCA setpoint changes in response to the market price will result in a redistribution of TCAs in on/off states and therefore change the probabilities for a unit to reside in each state. A randomly distributed load can be partially synchronized and the aggregated diversity lost. The loss of the load diversity can then create unexpected dynamics in the aggregated load profile. Raising issues such as restoring load diversity and damping the peak loads are also addressed in the paper.
This paper deals with optimal procurement of interruptible load services within secondary reserve ancillary service markets in deregulated power systems. The proposed model is based on an optimal power flow framework and can aid the independent system operator (ISO) in real-time selection of interruptible load offers. The structure of the market is also proposed for implementation. Various issues associated with procurement of interruptible load such as advance notification, locational aspect of load, power factor of the loads, are explicitly considered. It is shown that interruptible load market can help the ISO maintain operating reserves during peak load periods. Econometric analysis reveals that a close relationship exits between the reserve level and amount of interruptible load service invoked. It was also found that at certain buses, market power exists with the loads, and that could lead to unwanted inefficiencies in the market. Investing in generation capacity at such buses can mitigate this. The CIGRE 32-bus system appropriately modified to include various customer characteristics is used for the study.
This study describes a pilot effort to measure load reductions from a residential electric water heater (EWH) load control program using low-cost statistically based measurement and verification (M&V) approaches. This field experiment is described within the larger framework of overcoming barriers to participation of noninterval metered customers in Demand Response (DR) Programs. We worked with PJM Interconnection and a Curtailment Service Provider (CSP) to collect hourly load data for two substations and several hundred households over six weeks of load control testing. The experimental design reflected constraints imposed by limited funding, manpower, equipment, and the routine operation of the load control system by the CSP. We analyzed substation- and premise-level data from these tests in an attempt to verify several "point estimates" taken from the hourly diversified demand curves used by the CSP to establish aggregate load reductions from their control program. Analysis of premise-level data allowed for provisional verification that the actual electric water heater load control impacts were within a -60 to +10% band of the estimated values. For sub-station level data, measured values of per-unit load impacts were generally lower than the CSP estimated values for Electric Cooperative #2, after accounting for confounding influences and operational test problems. Based on this experience we offer recommendations to ISO and utility DR program managers to consider before undertaking further development of alternatives to the conventional but costly program-wide load research approach.
Outdoor temperature, thermal comfort level of consumers and payback load effect constrain direct load control (DLC) of air-conditioning loads and limit the DLC schedule. Since the constraints in direct air-conditioning load control are characteristics of air-conditioning loads, this work presents a novel group-DLC program with a least enthalpy estimation (LEE)-based thermal comfort controller for air-conditioning systems to control air-conditioning systems and eliminate DLC problems simultaneously. The g-DLC controller is the threshold for problems between air-conditioning units and the load management program, and arranges the DLC schedule for all air-conditioning units. The LEE-based thermal comfort controller can maintain the thermal comfort level within a reasonable range and prolong off-shift time of the DLC program, thereby increasing shedding load. It can also mitigate the impact of outdoor temperature and prevent payback load effect. Hence, DLC constraints on air-conditioning loads are mitigated.
This paper presents a multiperiod energy acquisition model for a distribution company (Disco) with distributed generation (DG) and interruptible load (IL) in a day-ahead electricity market. Assuming that cost information for individual generation companies (Gencos) and Discos is known, the Disco's energy acquisition strategy is modeled as a bilevel optimization problem with the upper subproblem representing individual Discos and the lower subproblem representing the independent system operator (ISO). The upper subproblem maximizes individual Discos' revenues. The lower subproblem simulates the ISO's market clearing problem that minimizes generation costs and compensation costs for interrupting load. The bilevel problem is solved by a nonlinear complementarity method. An 8-bus system is employed to illustrate the proposed model and algorithm. In particular, the roles of DGs and ILs to alleviate congestion are analyzed
As electricity markets are liberalized, consumers become exposed
to more volatile electricity prices and may decide to modify the profile
of their demand to reduce their electricity costs. This paper analyzes
the effect that the market structure can have on the elasticity of the
demand for electricity. It then describes how the consumers' behavior
can be modeled using a matrix of self- and cross-elasticities. It is
shown how these elasticities can be taken into consideration when
scheduling generation and setting the price of electricity in a pool
based electricity market. These concepts are illustrated using a
26-generator system
A state-queueing model of thermostatically controlled appliances
- L Ning
- D P Chassin
L. Ning, D.P. Chassin, A state-queueing model of thermostatically controlled appliances, IEEE Trans. Power Syst. 19 (August) (2004) 1666-1673.
Optimal scheduling of demand response events using options valuation methods
- R Tyagi
- J W Black
- J Petersen
R. Tyagi, J.W. Black, J. Petersen, Optimal scheduling of demand response events
using options valuation methods, in: Proc. IEEE PES General Meeting, July, 2011.
Power of choice-giving consumers options in the way they use electricity, Direction Paper
AEMC, Power of choice-giving consumers options in the way they use electricity, Direction Paper, March, 2012.
Power of choice-giving consumers options in the way they use electricity
AEMC, Power of choice-giving consumers options in the way they use electricity, Final Report, Sydney, November, 2012.
Beat the Peak-and Get Paid
- T M Demandsmart
- Australia
DemandSMART TM Australia Beat the Peak-and Get Paid [Online]. Available:
http://www.enernoc.com/for-businesses/demandsmart/in-australia
Competitive framework for procurement of interruptible load services
- L A Tuan
- K Bhattacharya
L.A. Tuan, K. Bhattacharya, Competitive framework for procurement of interruptible load services, IEEE Trans. Power Syst. 18 (May) (2003) 889-897.