Demand Response Market Research Articles

Review of Theoretical Research and Engineering Practice for Demand Response Market Mechanisms and Its Suggestions for China

As a substitute for the traditional generator units, the demand side resources can participate in peak-cutting and valley-filling in the power system by changing their operating power, which is called demand response (DR). The DR market mechanism will play a significant role in guiding power users to actively participate in DR. For a long time in the past, Chinese DR practice mainly relied on administrative methods. These methods have adapted to the monopoly power market environment, but it is difficult to adapt to the current state of the power system reform. Based on this background, this paper first summarizes the widely- used price-based DR market mechanism and incentive-based DR market mechanism. Besides, the point-based market mechanism recently proposed is analysed. Then, based on the theoretical research of the market mechanism, the DR engineering practice in the United States, Europe and China is introduced. The point-based market mechanism practice in Jiangsu Province is focused on. The advantages and disadvantages of each market mechanism are analysed. Finally, according to the current development situation of China’s power market, the suggestions about the DR mechanism are put forward for China.

Distributed demand response market model for facilitating wind power integration

To cope with wind power uncertainty, balancing authorities are required to procure adequate ancillary services (ASs) with the aim of maintaining the security of the power system operation. The transmission system operator (TSO) is responsible for maintaining the balance between supply and demand in delivery hours. Besides the generating units, demand response (DR) has the potential capabilities to be considered as a source of AS. The demand‐side AS can be used both locally (by the local entities in distribution networks) and system‐wide (by the TSO). However, the optimal coordination between the local and global beneficiaries is a challenging task. This study proposes a distributed DR market model, in which the DR is traded as a public good among the providers and beneficiaries through the local DR markets. The local DR markets can be run in each load bus to trade the DR provided by retail customers connected to that bus with the buyers. To include the interactions between the energy/reserve market and the local DR markets, a bi‐level programming model is proposed. The bi‐level problem is translated into a single‐level mixed‐integer linear programming problem using the duality theorem. The proposed model is verified by simple and realistic case studies.

Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.

Demand Response Strategy of Energy Prosumer Based on Robust Optimization Through Aggregator

In this article, the optimal operation strategy for the aggregator to participate in demand response (DR) market is proposed. First, on the day before the occurrence of DR, Customer Baseline Load (CBL)-based load forecasting is performed using historical load data and a day-ahead scheduling is implemented to minimize electric charges by using peak reduction and arbitrage trading. If the demand response occurs, distributed energy resources (DERs) bid power reduction capacity to the aggregator. In Korea tariff system, demand charges determined by peak load are very expensive. Therefore, DERs should not update their peak load due to demand response market participation. The uncertainty of load prediction is modeled using the average value of mean absolute percentage error (MAPE), and robust optimization (RO) is implemented to determine a bidding capacity, thereby preventing the peak form being updated due to prediction error. Then, the aggregator decides the capacity to participate in DR market by considering the bidding capacity and priority. It presents the method to determine the incentive for participation in DR using a logarithmic barrier function. To evaluate the performance of the proposed algorithm simulation was performed by constructing a scenario for prediction error and mandatory reduction capacity.

A General Sensitivity Analysis Approach for Demand Response Optimizations

In this work, we build upon existing literature on the demand response (DR) market, which consists of price-taking prosumers each with various appliances, an electric utility company and a social welfare optimizing distribution system operator, to design a general sensitivity analysis approach (GSAA) that can quantitatively estimate the potential of each consumer's contribution to the social welfare when given more resource capacity, such as allowing net-selling behavior. GSAA is based on existence of an efficient competitive equilibrium, which we establish in the paper. When prosumers' utility functions are quadratic, GSAA can give closed forms characterization on social welfare improvement based on duality analysis. Furthermore, we extend GSAA to a general convex settings, i.e., utility functions with strong convexity and Lipschitz continuous gradient. Even without knowing the specific forms the utility functions, we can derive upper and lower bounds of the social welfare improvement potential of each prosumer, when extra resource is introduced. For both settings, several applications and numerical examples are provided: including extending AC comfort zone, ability of EV to discharge and net-selling behavior. The estimation results show that GSAA can be used to decide how to allocate potentially limited DR market resources in a more impactful way.

A Stochastic Decision-Making Model for an Electricity Retailer With Intermittent Renewable Energy and Short-Term Demand Response

This paper presents a short-term decision-making model for an electricity retailer with self-production of renewable energy. In the proposed model, a new trading mechanism for short-term demand response (DR) between retail customers equipped with smart grid technology and an electricity retailer is presented. Through the proposed trading mechanism, retail customers submit short-term DR offer curves to the retailer in order to increase or decrease their energy consumption for a given time period at favorable prices. On the other hand, the retailer decides its involvement in the short-term DR market for every time period to avoid unfavorable prices in the real-time market and, thus, maximize its profit. The stochastic nature of day-ahead and real-time prices, renewable energy production, electricity demand, and DR participation of retail customers is taken into account in the formulation of this paper. The resulting model is a mixed-integer linear programming which can be solved by existing commercial solvers. Case studies using real-world data are performed to demonstrate the effectiveness of the proposed model.

Model Predictive Power Dispatch and Control With Price-Elastic Load in Energy Internet

The safety and stability of modern power systems are undergoing various challenges, introduced by the integration of fluctuating renewable generation. In this paper, we present a hierarchical model predictive power dispatch and control strategy for a class of modern power systems with price-elastic controllable loads (CLs) in energy Internet. In the upper-level optimization, a generalized multiperiod economic dispatch (GMPED) problem is organized within an electricity market environment aiming at maximizing the social welfare. Specifically, the price-elastic CLs are aggregated in controllable load aggregators (CLAs) to participate in the market competition. A novel utility function of the price-elastic CLAs is proposed for market demand response. By solving GMPED, the power setpoints of plants over the further periods are produced, as well as the real-time price for the optimal response of price-elastic CLAs. In the second-level operation, two types of model predictive control-based controllers for both the supply and demand sides are designed for power tracking control by considering the model of the power system and aggregated thermostatically controlled loads. Finally, two case studies are performed on the IEEE 14- and 39-bus system, respectively, which shows that the system-frequency deviation and system cost are reduced significantly with the proposed methods.

Optimal power allocation for residential network in islanded microgrid using capacity market demand response approach

This paper proposes a new demand response algorithm for load shedding and direct load control in smart homes with the goal of lowering peak-to-average ratios and enhancing power balance. The suggested algorithm makes use of two-way communication between distribution agents and smart homes (customers) to provide real-time load control, calculate percentage reductions, and produce distributed load consumption factors. An agent-based residential distribution network with four customers who owned smart appliances was modeled in MATLAB/SIMULINK to validate the technique. The findings show that the residential network is capable of reacting swiftly to supply changes and managing the grid's power balance through distributed load reduction, load shifting, and/or load adjustment, avoiding or limiting load shedding in the event of supply shortages.

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.

Networked Stackelberg Competition in a Demand Response Market

In the classical Cournot model, each firm tries to maximize its own payoff by deciding an optimal strategy for determining the quantity of goods produced during each time period–i.e. turn, of the game. In the typical application, all firms compete in the same market. In more recent economic models, firms compete across a number of markets simultaneously. In this situation, a Networked Cournot Competition (NCC) graph models the relationship between firms and markets. This paper describes a model of competition among demand response aggregators (DRAs) as firms to sell energy (as a homogeneous good) stored in aggregated residential batteries in a networked environment where market constraints are effected and trades are generally facilitated through the actions of a market maker who’s turn is sequentially distinct from the other players. We call this game Networked Stackelberg Competition (NSC). The impact of strategic anticipative behavior in networked markets is of paramount importance in distinguishing NSC from other competition models. For each firm, the optimal bidding strategy and Nash equilibrium are obtained through analyses of in an incomplete-information game. DRAs submit quantity bids and the market maker (system operator) controls the transaction power and transaction price over the network subject to transmission constraints and other market policies. Criteria required for existence of uniqueness of a Nash equilibrium are presented, and effectiveness of the game is also studied in the paper demonstrating demand response scheduling improves market situations. The details are presented in the application of a NSC model to real world case study data taken from the island of Maui, Hawaii.

Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market

It has been four years since the introduction of the Demand Response (DR) market in Korea. Although the DR market has been steadily increasing resource capacity and payments for demand resources, it cannot efficiently utilize DR resources under the current domestic policy system. In addition, research on the level at which the demand resource capacity registered in the DR market contributes to the reduction of peak demand is scarce. In this article, an algorithm that calculates the annual peak demand reduction contribution is developed considering the operational characteristics of DR resources in order to utilize the demand resources at a meaningful level. Also the peak demand reduction contribution is simulated for the next 15 years based on the historical electricity demand pattern in Korea and analyze the sensitivity of policy variables that affect the contribution of peak reduction. Finally, the critical policy constraint that affects the contribution of DR resources is identified and a method is proposed to improve the peak reduction contribution of DR resources. Related departments that operate the demand market can use this research as a reference for market operations.

Piloting Demand Response in Retailing: Lessons Learned in Real-Life Context

This article presents a case study on a demand response (DR) pilot project dealing with the application of DR in a grocery store with the utilization of refrigeration equipment as energy storage and photovoltaics (PV) as an energy source. DR has recently gained increased interest due to the growing penetration of intermittent renewable energy requiring flexibility in power consumption. The smart power grid enables the introduction of novel solutions to increase flexibility and the entrance of new actors into the markets. Developing new solutions for the mainstream markets requires experimentation in real-life settings serving the development of technological capabilities, necessary policies and regulation, and user and market needs, as well as adaptation of and to infrastructure and maintenance systems. Our case study on a DR pilot in a grocery store in Northern Finland focuses on how the project contributes to knowledge on the potential for DR and scaling up. It was found that energy efficiency, DR, and self-generated PV power can be aligned and even enhance the potential for DR. While mature technologies exist, applications and installations have not yet been standardized to enable rapid scaling up, and current DR market rules and practices fail to accommodate for small electricity consumers.

Contract Design for Aggregating, Trading, and Distributing Reserves in Demand-Side Frequency Regulation

With the integration of renewable energy sources to the power grid, the volatility of supply in the system will increase. Consequently, the mismatch between the power supply and demand may happen frequently and, thus, lead to frequency deviation from its nominal value. To avoid this scenario, demand-side flexibility has been widely considered to provide frequency regulation services. In this paper, we focus on the flexibility of thermal systems in buildings and propose a hierarchical demand-response market with a three-step algorithm to model the interactions among three entities: the independent system operators (ISOs), aggregators, and end users. The flexibility from the end users is aggregated in step 1, which is based on the incentive and electricity prices broadcasted by the aggregator. A robust optimization approach is adopted to improve the user's decision under the electricity price uncertainty. To model the interaction between the ISO and aggregators in step 2, a bilevel optimization problem is solved, in which the ISO seeks to minimize its cost, while the aggregators maximize their benefits in the day-ahead market. In step 3, each aggregator allocates its successful trading reserve among end users based on their performance scores.

Smart Charging for Grid-Connected Electric Vehicles to Provide Regulation Service

In the power system, grid-connected electric vehicles (EVs) have been considered as attractive resources that provide demand response (DR) and regulation service by controlling the charging power. However, due to the minimum resource size requirements in the electricity market, individual EVs are required to be aggregated by EV charging service providers (CSPs). The CSPs can minimize the charging costs of individual EVs by scheduling EV charging power considering electricity rates. At the same time, the CSPs benefits from the electricity market by providing the DR and regulation capacity. This paper proposes a systematic scheduling method for smart charging to maximize the benefits of CSP from the ancillary service (AS) market and demand response (DR) market as well as to minimize its charging cost. Numerical examples are also presented in order to demonstrate the effectiveness of the proposed scheduling model.

A real-time demand response market through a repeated incomplete-information game

Demand Response (DR) programs have been developed to help traditional power market to meet demand specially with increasing penetrations of renewable energies. This paper focuses on application of a game-theoretic framework to model competition between demand response aggregators to sell aggregated energy stored in storage devices directly to other aggregators in a market. This proposed market is cleared in each time interval of a day using a repeated game-theoretic framework. After finding optimal bidding strategies of the aggregators in each time interval, Dynamic Economic Dispatch (DED) is performed to update the dispatch of generators based on updated demand. Dynamic pricing has been considered in the proposed market framework in two forms: Real-Time Pricing (RTP) in each time interval of a day with updating demand and supply and Time-of-Use (TOU) with demand price-based scheduling through dynamic programing. The proposed method minimizes the fuel consumption and operation costs and optimally schedules the generation in grid's supply side. It also presents optimal prices during different periods simultaneously. Customers in light of the utility's optimal price minimize theirs electricity costs and optimally schedule their power consumption in order to participate in the DR market. The presented model is applied to IEEE 24-bus model.

Poster abstract: demand response market considering dynamic pricing

In this work, a power market framework is presented comprised of demand response aggregators (DRAs) trading energy stored in residential batteries. Competition is modeled by a Stackelberg game, with market clearing hourly, which determines each DRAs bidding strategy that will maximize payoff. The magnitude and price of power transactions allowable is controlled by the game’s leader. Dynamic pricing is considered in two forms. As demand updates, dynamic economic dispatch is used to update utility generator dispatch. The marginal electrical price offered by the utility updates each time interval reflecting updated supply and demand (real time pricing). Time-of-use load scheduling through dynamic programing combined with dynamic pricing optimizes load scheduling and reduces total demand in the system. The method schedules utility generation minimizing supply-side operational cost, and in turn, demand-side electricity cost at all times (valley, peak, off-peak) and is thus mutually advantageous.

Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market

Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability.

산업용 수용가의 에너지저장장치(ESS)를 이용한 수요자원 거래시장 참여 전략

산업용 수용가의 에너지저장장치(ESS)를 이용한 수요자원 거래시장 참여 전략

Determination of the optimal DR capacity for a commercial building with chiller system and energy storage system

Demand response (DR), which is used to shave the peak demand for securing supply reliability in a power system, is one of the promising constitutions for the end users. In a commercial building (COB), the most popular adjustable DR resource (DRR) is the chiller system. More specifically, its power consumption can be reduced by thermal mass control (precooling) in a specified DR duration. On the other hand, installation of energy storage systems (ESSs) is going on at an accelerated pace because of its high economic efficiency. ESSs have been used for peak‐shift operation under the time‐of‐use (TOU) tariff. However, ESS can be also used as an attractive DRR because it can reduce the net load by discharging the ESS for a specified DR duration. Based on this background, this paper presents a method for determining the optimal DR capacity in a COB with a chiller system and ESS. In the proposed scheme, the optimal DR capacity (kW) can be determined so that the total expected cost of a COB becomes minimum by using the chiller system and ESS while avoiding the DR penalty threat adopted in the Korean DR market. Uncertainty in the ambient temperature, the amount of reduction, and DR durations are considered as the scenarios. The estimation and economic evaluation of the proposed scheme is ascertained through case studies. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Towards a fully integrated market for demand response, energy and reserves

There is a trend for demand response (DR) market as a dedicated competitive environment for trading DR. In this market, aggregators participate as DR providers, while system operator, retailers and distributors are the DR buyers. Scheduling DR through the DR markets leads to a fair allocation of the benefits and payments across all participants. However, the integration of the DR markets into the existing power markets leads to technical and economic challenges. Those challenges associated with the integration of the DR markets into the energy/reserve markets are addressed in this study. To clear the DR markets jointly with the energy/reserve market, a bilevel approach is proposed in which the upper level belongs to energy/reserve market problem and the lower level includes DR market clearance. The proposed bilevel programming problem is then recast as a mixed-integer linear programming problem which can be solved using commercially available software. Finally, numerical results are provided to illustrate the performance of the proposed approach, demonstrating it brings about lower reserve price and higher social welfare compared with the existing markets.