Demand Response Market Research Articles

Strategic joint bidding and pricing of load aggregators in day-ahead demand response market

Load aggregators (LAs) purchase demand response (DR) capacity from users by incentive compensation and then resell them by bidding in the day-ahead DR market to gain profits. Existing related research fails to adequately integrate bidding and pricing decision-makings and lacks sufficient consideration of uncertain user response behaviors, resulting in unstable trading returns. To this end, we propose a novel joint optimization method for bidding and pricing of the LA. First, a two-stage joint optimization framework is constructed to coordinate the temporally coupled bidding and pricing decisions, where these two strategies are simultaneously optimized to form the DA bidding strategy at first, and the pricing adjustment is then introduced after market-clearing to develop the formal user incentive scheme. Second, to account for the risks from uncertain response behaviors, the first stage incorporates DR settlement rules and the hedging effect of the aggregated response uncertainty to develop a relatively aggressive bidding strategy. After that, a user selection mechanism is then introduced to filter out users with both low response costs and reliable responses for participation. Numerical studies are presented to validate the proposed methodology in formulating optimal trading strategies that effectively tradeoff transaction revenue and risks.

Integrated energy cluster hierarchical regulation technology considering demand response

Integrated Energy Cluster (IEC), the regional aggregation of integrated energy systems (IES), has accumulated plenty of dispatchable resources with the development of energy market. This, while significantly providing system Demand Response (DR) potential, also complicates the interaction of the IEC with the main grid and increases the difficulty of system scheduling. To address this issue, this paper proposes a Reinforcement Learning-driven multi-agent hierarchical regulation framework that makes full use of DR to maximize the benefits of both IEC and main grid. Firstly, in the context of the DR market, a mechanism for IECs to bid in the real-time DR market is proposed. Furthermore, an "IEC-main network" hierarchical regulation model taking account of DR is established to minimize the IEC operation cost and maximize the societal benefit. Moreover, an optimization algorithm utilizing Deep Deterministic Policy Gradient (DDPG) with Multi-process (MP) and Priority Experience Replay (PER) mechanism is proposed to allow adaptability to high-latitude and large-scale applications. In case study, the proposed model and algorithm is tested on an 8-node system and a 24-node system. The result indicates that the hierarchical regulation model considering DR can improve the system economy by 2.59 % than that without DR and the improved DDPG algorithm can enhance training effectiveness in comparison with DDPG and PER-DDPG.

Application of deep reinforcement learning in electricity demand response market: Demand response decision-making of load aggregator

With the large-scale integration of renewable energy generation, developing the potential of demand response is becoming more and more significant. However, the number of consumers is huge, the electricity consumption behaviors are various and the information of consumers is incomplete, thus there are great difficulties in modeling and processing demand response by the conventional mathematical methods. Therefore, how to accurately predict the response behaviors of consumers and select the appropriate consumers for demand response is worthy of in-depth discussion. In this paper, the decision-making method of the load aggregators who participant in demand response market on behalf of the small and medium-sized consumers based on Deep Q-network algorithm is proposed, supporting the aggregators to properly guide the potential demand response consumers. At the same time, a dynamic consumer classification method based on self-organizing maps algorithm is also proposed, supporting the aggregators to accurately predict the response behaviors of the consumers. Simulation results show that the proposed method can effectively realize the classification of the consumers and result in a more beneficial demand response.•The proposed method does not require complete information, such as demand response behavior parameters of the consumers.•Self-organizing maps can realize a dynamic classification of demand response consumers as the case may be.•DQN algorithm can effectively realize the demand response decision-making of aggregators under the condition of incomplete information.

Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators

In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.

Online transfer learning-based residential demand response potential forecasting for load aggregator

Accurate demand response (DR) potential forecasting is the basis for load aggregators (LA) to make optimal bidding strategies in DR market trading. LAs usually face practical challenges when they perform forecasts for those new customers who have no historical response data. Transfer learning provides a promising solution to this problem by leveraging knowledge acquired from other existing contracted customers. However, traditional transfer learning methods are trained offline and cannot make use of the latest response information of these new customers, which may result in large forecasting errors since the response behavior of new customers usually dynamically changes. To address the above issues, this paper proposes an online transfer learning-based DR potential forecasting framework, in which two forecasting models are established. The first one is built using the historical data of existing customers and this model is then transferred to the target domain (i.e., new customers) by parameter sharing and fine-tuning. The second model is built using the local response data of new customers, which gradually accumulates with the increasing participation of DR events. These two models are combined by an adaptive ensemble framework based on their online performances, thus enabling it to dynamically track the changes in new customers' response behavior. Case studies on a real-world dataset validate the effectiveness of the proposed framework.

Data-driven model for real-time evaluating capacity of commercial buildings to participate in the demand response

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.

The potential of demand response as a tool for decarbonization in the energy transition

Planning and managing energy production, transmission, and distribution face new challenges as renewable energy sources become more widely used. These sources enhance existing issues of grid operation due to their unpredictability and intermittency. As a result, sophisticated energy system control techniques and technologies that offer novel flexibility solutions to the inelastic demand side are necessary to ensure the power system’s stability. This paper reviews the feasibility of demand response as a tool for effective grid management and a measure for properly sized renewable energy systems (RES) capacities, both of which could accelerate the phase-out of fossil fuels. Research also focuses on tapping into the building stock's demand response capacity in an effort to decrease operational CO2 emissions. In this line of approach, current status and goals of the respective European policies are analyzed. The role of market based decarbonization is studied and presented along with the status of demand response market implementation in several countries.

Machine learning based energy demand prediction

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

A classified retail pricing and production scheduling model for industrial customers

In the electricity retail market, it’s an important operation way for a retailer to provide diversified services and design appropriate retail pricing strategies that stimulate demand side responses in order to increase profit and maintain customer welfare. This paper proposes a novel retail contract design that a retailer can adopt to provide different time-of-use (TOU) prices for the various types of industrial customers based on their adjustabilities and a part of rewards from the demand response (DR) market to those who respond to the urgent electricity requirements. A bi-level optimization model of retail pricing for classified industrial customers is set up based on their adjustable capacities of production process operations. At the upper level, the retailer’s benefit is maximized considering the stochastic risks of load fluctuation and the spot market. This is achieved based on the electricity consumption strategies of classified industrial consumers, which are obtained from the lower level. Decisions are then made concerning purchases from the spot market and retail pricing strategies for industrial customers. Based on the retail prices from the upper level and industrial production processes modeling, the model at the lower level takes the maximum industrial user’s benefit as the objective to obtain the electricity consumption scheme of each type of industrial user. The model optimizes both the time division and price level of retail TOU prices, resulting in detailed electricity consumption strategies for all types of industrial customers. The results of the cases show that the proposed retail pricing can improve adjustable capacities of industrial production process operations, effectively smooth the load curve of the retailer by making full use of the peak-load-staggered among industrial customers, and boost profits of both the retailer and industrial customers. Thus, a mutually beneficial situation between the retailer and industrial customers is realized.

A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective of China

Faced with the growing renewable energy requirements, there is increased interest in cross-region of large-scale renewable energy market, which provides an alternative path for building sustainable power systems. Critically, the development of a renewable energy-dominated electricity market is an important way to achieve global climate goals and energy conversion. Despite improved achievement of electricity demand response (EDR) market, only limited development in the coordination capacity and development scale of EDR are dominated by renewable energy. Moreover, the normal operation and work efficiency of the system is greatly affected due to data transmission errors and other human factors. Thus, it is of great importance for the State Grid to accurately identify the data sources and realize the interactive development of cross-region power systems. This paper presents a new cross-region (province) electricity demand response (CR-EDR) model in China for large-scale renewable energy participating in EDR market. This model is applied to three provinces in China based on renewable energy, and fully consider how wind power is integrated with EDR in terms of operation, grid connection and optimization. Presently, China is in the initial stage of development, and the identification of data anomalies is inevitable for the CR-EDR. To solve this problem, a novel machine learning (ML)-based approach is proposed for effective identification of EDR. By calculating wind power output and customers’ EDR results, reliable feature sequences can be obtained. Finally, all the feature sequences are uploaded to cross-region system operators (CR-SO) for classification and identification by ML. In the case studies and discussions, we integrated all feature sequences to CR-SO for identification and present a novel process approach to implement EDR, the random forest (RF) enables 100% accuracy for training set and 99.7685% for testing set with small training samples. Compared with RF, support vector machine only achieves 79.1667% for testing set with small training samples. With accurate RF identification results, the stable operation capability and management level of the system can be effectively improved. The proposed methodology creates a new provincial perspective of China and the establishment of CR-EDR, which provides a theoretical and methodological guidance for all countries and regions to develop CR-EDR.

Evaluating long-term profile of demand response under different market designs: A comparison of scarcity pricing and capacity auction

Demand response (DR) is widely acknowledged as a promising solution to maintain the capacity adequacy of the power system. In China, DR is now shifting from government pricing to market-oriented pricing. However, the market pricing mechanism to be adopted is still inconclusive and the long-term influence of different pricing mechanisms on incentivizing DR remains unknown. Moreover, the market contains multiple participants, various strategic behaviors, and non-linear clearing procedures while traditional optimization models cannot capture the complex dynamics. This paper adopts a system dynamic (SD) method to evaluate the long-term profile of DR under different market designs. Firstly, an investment decision model of generation companies is formulated to simulate the growth tendency of capacity supply in a long-term period. Secondly, in the context of unmatured marketization, the growth tendency of DR and retail market users are modeled by Bass diffusion. Finally, scarcity pricing and capacity auction are applied to investigate the incentive influence of different market designs on DR. Through the simulation to 2035, the results show that compared with the energy-only market, scarcity pricing, and capacity auction can raise the capacity of DR from 3.12% to 7.58% and 9.21%, respectively.

Stochastic trading of storage systems in short term electricity markets considering intraday demand response market

In recent years, the renewable energy sources are used in the power system for reducing environmental pollution. One of these renewable energy sources is wind farm that is used widely. However, they increase the uncertainty of the power system. To solve this problem, plenty researches have proposed several methods that have some disadvantages. This paper tries to decline the effects of uncertainty of wind turbines by using of the energy storage systems and optimal demand response programs. A stochastic planning of energy storage systems and wind generation are presented in the short term electricity markets considering the trading day-head and intraday market of demand response. In order to formulate an accurate model of various components of system, all needed constraints are considered. In this problem, wind turbines, energy storage systems, and demand response aggregator work coordinately that is called virtual power plant. For better comparison, two states consist of coordinated and uncoordinated working of all components are considered and assessed. The various scenarios for electricity market price, adjustment market price, imbalance energy price, and the generated power of wind turbine are considered. These scenarios are generated with hybrid neural network and coati optimization algorithm. Also, the iterative fast progressive Kantorovich method is used for scenario reduction. The results show the better response of the proposed method in optimal operation of virtual power plant.

A State-Based Potential Game-Theoretic Approach for Massive Residential Consumers Participating in Demand Response

In order to realize massive residential consumers participating in demand response (DR), load aggregator (LA) can be employed to aggregate flexible loads. In this article, a distributed optimization approach based on state-based potential game (SPG) is proposed to describe the bidding behavior among LAs in day-ahead DR market for load shifting amount. Under the consideration of DR resource's energy consumption satisfaction, profit-oriented bidding decision problem is modeled from the perspective of individual LA and DR market. Basically, the establishment and proof of an SPG model is presented in details. Then, the best-strategy-response iterative algorithm is proposed to search for the Nash equilibrium in a distributed way. Finally, simulation is carried out to verify the efficiency and effectiveness of the proposed approach. Simulation results show that the whole profit of all LAs increases 0.15%, while the running time of the algorithm reduces 59.53% compared with the conventional algorithm in the noncooperative game.

The aggregator’s contract design problem in the electricity demand response market

The aggregator’s contract design problem in the electricity demand response market

Joint game-theoretical optimization for load aggregators in demand response market considering the breach of residential consumers

Demand response (DR) with the participation of load aggregator (LA) has received extensive attention in recent years due to the increasing energy demand. However, LA has to face the risk that consumers may refuse to be controlled by LA due to the uncertainty of energy consumption on demand side. Therefore, this paper proposes a joint game-theoretical optimization for LAs in DR day-ahead market and intraday market considering the breach of residential consumers. In day-ahead market, LA will compete with other LAs and obtain the optimal bidding amount through a non-cooperative game process, to obtain the maximal self-profit. In intraday market, in order to make up for the breach amount of consumers, DR resource-deficit LAs can purchase resource from DR resource-surplus LAs via Nash bargaining process. Basically, Nash bargaining model is formulated and solved by translating the optimization problem into two sub-problems. Finally, a case study is performed to show the effectiveness of the proposed DR framework. Simulation results show that the whole profit of all LAs increases 25.9% compared with the scenario where LAs only participate in day-ahead market and will be punished by DR market due to the bidding breach.

Multi-level distributed demand response study for a multi-park integrated energy system

Current studies of integrated demand response (IDR) across multiple campuses often use centralized, unified scheduling with individual campuses as the object of analysis, which ignores the independent autonomy of users within the park. To this end, this paper proposes a multi-level distributed demand response model for a multi-park integrated energy system, which is solved using a combination of the particle swarm optimization algorithm and mixed integer linear programming software. First, based on the existing two-part tariff, an energy management system is introduced to construct a park integrated demand response market based on a noncooperative game. Second, load aggregators are used as a link to form an optimized alliance of multiple parks, and the overall economy of the coalition is optimized through a cooperative game of multiple parks. The model allows for coordination and complementarity based on zonal autonomy, maximizing economic efficiency and exploring the demand response capability of users. Finally, an arithmetic simulation is carried out with a scenario of a day-ahead electricity peaking auxiliary service market, and the results verify the feasibility and economics of the designed model in a scenario where multiple parks participate in integrated demand response.

Perception and decision-making for demand response based on dynamic classification of consumers

Demand response plays a significant role in improving electricity market efficiency and keeping power system stability. Load aggregators can provide reliable resource for power balance and auxiliary services by integrating dispersed responsive loads. In order to meet the aggregators’ requirement of timeliness and accuracy in the real-time demand response market, a method of perception and decision-making for demand response based on dynamic classification of consumer is proposed. The price elasticity of electricity demand is calculated based on continuously updated trading experience and applied as a classification criterion. Consumers are dynamically classified by self-organizing maps algorithm to perceive consumers’ responsive ability. Furthermore, the interaction model of aggregator and consumers in market environment is constructed, and deep reinforcement learning is applied to solve the trading strategy in the uncertain market with incomplete information. Simulations show that the results of trading partner and reward price gained by the proposed method are appropriate. Therefore, the deviation between response quantity and the bid-winning volume is effectively controlled, and the transaction revenue of the aggregator is improved.

A Two-Stage Mechanism for Demand Response Markets

Demand response involves system operators using incentives to modulate electricity consumption during peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers’ baselines, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer’s electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the customers and can potentially lead to overpayments to customers who do not reduce their consumption and under payments to those who do. Moreover, optimal power consumption reductions of the customers depend on the costs that they incur for curtailing consumption, which in general are private knowledge of the customers, and which they could strategically misreport in an effort to improve their own respective utilities even if it deteriorates the overall system cost. The two-stage mechanism proposed in this letter circumvents the aforementioned issues. In the day-ahead market, the participating loads are required to submit only a probabilistic description of their next-day consumption and costs to the system operator for day-ahead planning. It is only in real-time, if and when called upon for demand response, that the loads are required to report their baselines and costs. They receive credits for reductions below their reported baselines. The mechanism for calculating the credits guarantees incentive compatibility of truthful reporting of the probability distribution in the day-ahead market and truthful reporting of the baseline and cost in real-time.

A prospect-theoretic game approach to demand response market participation through energy sharing in energy storage systems under uncertainty

The Paris Agreement, which is carried out in a bottom-up manner, is giving great vitality to the transition to low-carbon energy system. Accordingly, as the spread of renewable energy increases, securing flexibility resources is emerging as a keyword in the power system and power market, and many studies on demand response (DR) and energy storage system (ESS) classified as demand-side flexible resources are being conducted. However, most of the studies on DR are on voluntary DR programs that participate through bidding among incentive-based DR or price-based DR programs, and ESS faced difficulties in its distribution due to low economic feasibility. Therefore, in this work, we propose a feasible process for participating in a reliability DR program that guarantees high profitability but involves high uncertainty, and non-cooperative game model is suggested to mitigate the uncertainty through surplus electricity-trading model of ESS. In addition, a subjective decision-making based on prospect theory is proposed to reflect the impact of the uncertainty about the incentive-based program’s engagement. Case study results performed in an environment formed based on actual operation data of the Korean DR market prove that participants’ uncertainty is mitigated when participating in the DR market through power trading of ESS through the proposed model. In addition, a small-scale ESS with an average benefit–cost ratio of 0.78 improves by up to 1.08 (1.04 with a standard scenario).

Market strategy options to implement Thailand demand response program policy

DR (Demand Response) is an energy management strategy for reducing electricity usage during high-demand and unbalanced problem between demand and supply, which can lead to reduction in power system investment costs and CO2 emission. This technology is especially suitable for the initial state of the power system transformation and demonstrates a good attitude in sustainable development view. DR also has the potential to improve the flexibility of Thailand power system in order to promote the growth of renewable energy (RE). This study analysed a DR business option where LAs (Load Aggregators) are the key actors that collect and managed committed DR resources. Our study proposed the establishment of LA#1, as lead and first level LAs, wherein LA#1 consists of state utilities. For non-utilities interested in the DR business, they will be designated as LA#2, or level two LAs, responsible for DR resources left out by LA#1. The study included an assessment of the DR market size, and analyses of our proposed DR business solution. The SWOT analysis showed that the strengths and opportunities outweighed the weaknesses and threats for a business option where the state utilities are the lead LAs and the ones primary doing the DR business. The BMC assessment provided a comprehensive analysis confirming that our proposed LA business operation, wherein having an LA#1 consisting of state utilities, should be the preferential DR business operation option under the current power market structure. The financial analysis also supported this business option of state utilities being LA#1.