Demand Response Pricing Research Articles

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

Hybrid-timescale optimal dispatch strategy for electricity and heat integrated energy system considering integrated demand response

An integrated energy system realizes multi-energy management and multi-load integrated supplement. However, because of the uncertainties of the renewable energy and loads and different dynamic characteristics of multi-energy, the efficiency of the system is constrained. To ascend the flexibility, a hybrid-timescale dispatch strategy considering integrated demand response is proposed. Firstly, a deep integrated demand response strategy is proposed using price demand response and ground source heat pump. Secondly, a linear dynamic model of the heat network is adopted to calculate the real-time energy transfer status in the heat pipeline, reflecting the thermal delay. Thirdly, to embed the linear dynamic model into the optimal scheduling process, a hybrid-timescale optimal dispatch framework for electricity and heat integrated energy system is proposed. Simulation results on MATLAB show that compare to the system that does not consider integrated demand response, the wind desertion, user cost and operation cost are reduced by 16.4 %, 0.85 %, and 2.72 %, respectively. And compared with the system only using energy storage, the planning cost is reduced by 47.0 %. By introducing the IDR strategy, and adopting the dynamic model of heat system, the flexibility of the system is released and the balance between economic cost and energy efficiency are achieved.

The application effect of the optimized scheduling model of virtual power plant participation in the new electric power system

To build a comprehensive framework for virtual power plant (VPP) development aligned with market dynamics and to devise effective strategies to foster its growth, this study undertakes several key steps. Firstly, it constructs a VPP development framework based on market conditions, to drive the evolution of new power systems and facilitating energy transformation. Secondly, through a blend of theoretical analysis and model construction, the fundamental principles of VPP are systematically elucidated, and a decision model for the VPP development framework, focusing on price demand response, is formulated. Lastly, an optimal scheduling model for the new power system is developed, with its efficacy validated across three distinct scenarios. The findings underscore the critical importance of integrating energy storage technologies, particularly pumped storage hydropower systems, for achieving balance and optimization within new power systems. Model verification reveals that the incorporation of energy storage power stations significantly enhances system stability and efficiency, particularly in addressing the volatility associated with renewable energy sources. Additionally, the analysis indicates that while the adoption of energy storage technologies may marginally increase overall power generation costs, the total power generation cost declines with the integration of battery storage and pumped storage hydropower stations. This suggests that leveraging energy storage technologies not only enhances system operational reliability but also contributes to reducing the overall cost of power production to a certain extent. In summary, this study presents an economic and environmentally sustainable scheduling model for new power systems within the context of market trading environments. By offering both theoretical insights and practical guidance, it aims to support sustainable development and energy transformation initiatives. Ultimately, the study is poised to foster the adoption of clean energy, facilitate the establishment of smart grids, and bolster the sustainable utilization of energy resources, thereby advancing environmental conservation efforts.

Energy consumption optimization of chiller plants with the genetic algorithm based GWO and JAYA algorithm in the dynamic pricing demand response

Demand response is a program in the electrical system that is used to increase or decrease the energy demand of the consumers where the consumers can actively respond to the system requirements. We developed an energy scheduling system to schedule the consumer load in a dynamic pricing scheme to reduce the cost and lower the peak demand. The problem formulation is done to model the food storage chiller plant for cost minimization with the consumer constraint. The optimal schedule is to be generated in a real time varying electricity price scheme. To solve this optimization problem Grey wolf optimizer (GWO) algorithm and JAYA optimization algorithms are used. Besides, the Genetic algorithm (GA) based GWO and GA based JAYA algorithm are developed and simulated. The genetic algorithm improves the population diversity of the solutions. The simulation results show a 22 % cost reduction and 10 % energy consumption reduction as compared with normal control method and consumer constraints are satisfied. The algorithms GA-GWO and GA-JAYA give better results for optimal cost minimization in terms of the reduced cost, energy consumption, energy consumption shift to low electricity price hours and temperature maintained below the mean set temperature through the entire duration.

Deep reinforcement learning based dynamic pricing for demand response considering market and supply constraints

This paper presents a Reinforcement Learning (RL) approach to a price-based Demand Response (DR) program. The proposed framework manages a dynamic pricing scheme considering constraints from the supply and market side. Under these constraints, a DR Aggregator (DRA) is designed that takes advantage of a price generator function to establish a desirable power capacity through a coordination loop. Subsequently, a multi-agent system is suggested to exploit the flexibility potential of the residential sector to modify consumption patterns utilizing the relevant price policy. Specifically, electrical space heaters as flexible loads are employed to cope with the created policy by reducing energy costs while maintaining customers' comfort preferences. In addition, the developed mechanism is capable of dealing with deviations from the optimal consumption plan determined by residential agents at the beginning of the day. The DRA applies an RL method to handle such occurrences while maximizing its profits by adjusting the parameters of the price generator function at each iteration. A comparative study is also carried out for the proposed price-based DR and the RL-based DRA. The results demonstrate the efficiency of the suggested DR program to offer a power capacity that can maximize the profit of the aggregator and meet the needs of residential agents while preserving the constraints of the system.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

Recursive Learning Based Smart Energy Management With Two-Level Dynamic Pricing Demand Response

Recursive Learning Based Smart Energy Management With Two-Level Dynamic Pricing Demand Response

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Graph Deep-Learning-Based Retail Dynamic Pricing for Demand Response

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily en-ergy consumption behaviors. It helps manage flexible de-mand response resources on peak load. However, it is insuf-ficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mecha-nism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers energy consumption behaviors under different prices. Then, to avoid unfair eval-uation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumers time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superior-ity of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

Optimization game study of long-term dispatch for combined electricity and gas operation considering integrated demand response

The combined operation of electricity and gas is an important way of an integrated energy interconnection in the future. To meet the demands of multiple interests in the joint operation of electricity and gas, the master-slave game theory model is developed. In game model the power supply company and the gas company are the master players and the household load aggregation cluster is the slave player. The multi-energy joint load characteristics of household load aggregation and the uncertainty of the price demand response are used to obtain a payment function for each party to the game. The master-slave game model is built with the objective of maximizing the benefits on the supply side and minimizing the payments on the demand side. The master uses price as the strategy set and the slave uses demand response as the strategy set. The tripartite game equilibrium solution is screened by an optimal response function combined with Non-dominated sorting genetic algorithm (NSGA). Through the simulation analysis of an example, the frontier solution set of the tripartite game is obtained. Subjective scoring method and K-means clustering algorithm are used to select the optimal solution and discuss the application of the model under the influence of photovoltaic and gas turbine, the application of master-slave game and traditional NASH equilibrium solution, the application of game and general objective optimization in this paper. The model can be used to optimize the economic benefits of the parties in the long-term operation dispatch, and provide a reference for the optimal operation of market decisions under energy interconnection.

Customer-Centered Pricing Strategy Based on Privacy-Preserving Load Disaggregation

Demand response (DR) is a demand reduction or shift of electricity use by customers to make electricity systems flexible and reliable, which is beneficial under increasing shares of intermittent renewable energy. For residential loads, thermostatically controlled loads (TCLs) are considered as major DR resources. In a price-based DR program, an aggregation agent, such as a retailer, formulates price signals to stimulate the customers to change electricity usage patterns. The conventional DR management methods fully rely on mathematical models to describe the customer’s price responsiveness. However, it is difficult to fully master the customers’ detailed demand elasticities, cost functions, and utility functions in practice. Hence, in this paper, we proposed a data-driven non-intrusive load monitoring (NILM) approach to study the customers’ power consumption behaviors and usage characteristics. Based on NILM, the DR potential of the TCLs can be properly estimated, which assists the retailer in formulating a proper pricing strategy. To realize privacy protection, a privacy-preserving NILM algorithm is proposed. The proposed methodologies are verified in case studies. It is concluded that the proposed NILM algorithm not only reaches a better prediction performance than state-of-art works but also can protect privacy by slightly sacrificing accuracy. The DR pricing strategy with NILM integrated brings more profit and lower risks to the retailer, whose results are close to the fully model-based method with strong assumptions. Furthermore, a NILM algorithm with higher performance can help the retailer earn more benefits and help the grids better realize DR requirements.

Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction

The virtual power plant (VPP) plays an important role in managing distributed energy by integrating renewable energy sources, energy storage systems and dispatchable loads. It can not only provide peak regulation services as good flexible resources, but also participate in the electricity market for additional profit. This paper presents a multimarket model to develop an optimal bidding strategy for VPP. To enhance the effectiveness of demand response, a fixed time of use price is converted into a dynamic response price. The integrated consumption satisfaction is quantified from both comfort and economy perspectives. Multi-objective optimization is carried out to maximize market profit of VPP as well as consumer satisfaction. The results show that: (1) Compared to time of use prices, VPP's profit and consumers' satisfaction level increased by 12.46% and 3.26% respectively under dynamic response prices. (2) The increase in the market profit of VPP is accompanied by a gradual decline in integrated consumption satisfaction. There are two inflection points in the process of decline, occurring at satisfaction levels of 1.01 and 0.98 respectively. (3) The multi-objective optimization strategy can achieve a win-win situation for both its operator and the internal consumers.

Scheduling optimization of a wind power-containing power system considering the integrated and flexible carbon capture power plant and P2G equipment under demand response and reward and punishment ladder-type carbon trading

At present, coal-fired thermal power still dominates in China, and as the environmental pollution problem worsens, it is critical to find the optimal combination of coal and new energy to reduce CO2 emissions and promote new energy consumption. Based on this, this paper considers a reward and punishment ladder-type carbon trading mechanism, firstly, unit transformation of the conventional coal power plant (CPP) on the power side, forming the integrated and flexible carbon capture power plant (IFCCPP), and constructing a coupling model of the IFCCPP with power to gas (P2G) equipment and wind farms(WFs), forming an IFCCPP-P2G-WF system; secondly, introducing a price demand response mechanism on the load side, and establishing a two-stage scheduling model for the IFCCPP-P2G-WF system, with the first stage model aiming at the minimum sum of squared load fluctuations and the second stage model aiming at the minimum operation cost. Finally, the effectiveness of the scheduling model proposed in this paper is validated by comparing and analyzing the scheduling results under eight scenarios. When compared to other models, the proposed model significantly increases wind power consumption, reduces carbon emissions, and lowers operation cost, all of which have advantages.

Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations in managing an increasing demand and predicting future pricing. This paper presents a novel approach to address the limitations of existing methods for determining and controlling demand response (DR) in the context of dynamic pricing systems for sustainable energy development. By leveraging actor–critic agent reinforcement learning (RL) techniques, a dynamic pricing DR model is proposed for efficient energy management. The model’s learning framework was trained using DR and real-time pricing data extracted from the Australian Energy Market Operator (AEMO) spanning a period of 17 years. The efficacy of the RL-based dynamic pricing approach was evaluated through two predicting cases: actual-predicted demand and actual-predicted price. Initially, long short-term memory (LSTM) models were employed to predict price and demand, and the results were subsequently enhanced using the deep RL model. Remarkably, the proposed approach achieved an impressive accuracy of 99% for every 30 min future price prediction. The results demonstrated the efficiency of the proposed RL-based model in accurately predicting both demand and price for effective energy management.

Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand Response

This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems.

Futures Market for Demand Responsive Travel Pricing

While transportation funding can be collected in a variety of direct (e.g., fares, tolls, and gas taxes) or indirect (e.g., property and sales tax) ways, dynamic demand responsive pricing not only collects revenue but also incentivizes travelers to avoid peak-demand periods, thus utilizing infrastructure capacity more efficiently. Unfortunately, the demand response to price changes, called the price elasticity of demand, is generally greater for longer-term travel planning (e.g., air and rail travel) than it is for more atomized short-term planning (e.g., highway tolls and transit fares). While this is caused by a plethora of factors (e.g., time flexibility, housing choice, automobile investment, etc.), a critical factor is that travelers simply lack sufficient information for future travel planning. For example, airline prices at different times can easily be compared, but a highway driver cannot accurately predict congestion nor congestion pricing. For this reason, such price changes have little effect on demand. This leaves any congestion abatement up to inefficient trial and error, and anecdotal speculation by travelers. Moreover, dynamic pricing is politically unsavory because of price uncertainty and collateral equity concerns. This article seeks to help remedy these concerns by proposing a simple “futures” market mechanism that can augment existing fare/toll collection technologies, providing travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices. Users can optionally pre-pay their future fares/tolls to lock in a lower price for expected trips, thus encouraging good travel planning and efficient infrastructure utilization, while reducing price uncertainty.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use (TOU) strategy can effectively improve the energy consumption mode of customers, reduce the peak-valley difference of load curve, and optimize the allocation of energy resources. This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use. First, this study proposes a period partitioning model, which is based on a moving boundary technique with constraint factors, and the Dunn Validity Index (DVI) is used as the objective to solve the period partitioning. Second, a control strategy for the curtailable flexible load is investigated, and a TOU strategy is utilized for further modifying load curve. Third, a price demand response strategy for adjusting transferable load is proposed in this paper. Finally, through the case study analysis of typical daily flexible load curve, the efficiency and correctness of the proposed method and model are validated and proved.

Region-wise evaluation of price-based demand response programs in Japan’s wholesale electricity market considering microeconomic equilibrium

Real-time pricing demand response programs (RTP-DRPs) are practical measures that ensure the end user's profitability from using electricity by adjusting the supply and demand equilibrium without activating costly solutions. This study explores the potential of RTP-DRPs by developing and applying a region-wise modeling approach based on maximizing the end user's social welfare in the wholesale electricity market in Japan. The regions of the wholesale market are classified based on their response into regions with excess supply, regions with high demand burden, and regular suppliers of inter-regional connections. The results revealed that the RTP-DRPs could potentially reduce the peak demand of the residential sector in Chubu, Chugoku, Kansai, Kyushu, Tokyo, and Tohoku by 1.91%-7.81%. Meanwhile, in Hokkaido, Hokuriku, and Shikoku, by 16.13%-22.9%. The avoided greenhouse emission (GHG) in Tokyo is estimated to be 82.6 and 192.2 tons in summer and winter, respectively.

Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

Capacity optimization of hybrid energy storage system for microgrid based on electric vehicles’ orderly charging/discharging strategy

The high penetration rate of electric vehicles (EVs) will aggravate the uncertainty of both supply and demand sides of the power system, which will seriously affect the security of the power system. A microgrid (MG) system based on a hybrid energy storage system (HESS) with the real-time price (RTP) demand response and distribution network is proposed to deal with uncertainties. Through the guidance of RTP, the electricity consumption behavior of consumers and car owners is more adaptable to the output uncertainty of renewable energy source (RES) to ensure that the system has sufficient elastic capacity within the schedule cycle. The bi-level programming optimization model of HESS capacity is established, maximizing the MG's reliability and minimizing the comprehensive operating cost (COC). In addition, an EV's orderly charging/discharging strategy is formed, which effectively reduces operating costs and peak-to-valley load differences. The results show that EVs can effectively mitigate the peak-to-valley load difference by 20.5% under 100% participation in orderly charging/discharging. Under RTP-based demand response, MG can reduce the COC by 25.5%. In addition, the number of EVs participating in vehicle-to-grid also has different effects on scheduling. Based on the experimental data from Guizhou, results show that 200 electric private vehicles (EPVs) and ten electric buses (EBs) are the optimal number of vehicles to be solved; namely, the optimal capacity of the HESS is achieved.