Price-based Demand Response Research Articles

Research on Optimal Scheduling of Multi-Energy Microgrid Based on Stackelberg Game

In recent years, rapid industrialization has driven higher energy demand, depleting fossil-fuel reserves and causing excessive emissions. China’s “dual carbon” strategy aims to balance development and sustainability. This study optimizes microgrid efficiency with a tiered carbon-priced economy. A Stackelberg game establishes microgrid-user equilibrium, solved iteratively with a multi-population algorithm (MPGA). Comparative analysis can be obtained without considering demand response scenarios, and the optimization cost of microgrid operation considering price-based demand response scenarios was reduced by 5%; that is 668.95 yuan. In addition, the cost of electricity purchase was decreased by 23.8%, or 778.6 yuan. The model promotes user-driven energy use, elevating economic and system benefits, and therefore, the scheduling expectation of “peak shaving and valley filling” is effectively realized.

Low-carbon economic dispatch of integrated energy systems that incorporate CCPP-P2G and PDR considering dynamic carbon trading price

To address the challenge of global warming stemming from excessive fossil fuel combustion, the exploration of low-carbon technologies and improvements to carbon trading mechanisms stand out as vital strategies. An effective conduit for these strategies is the integrated energy system (IES), which has garnered increasing attention for its potential. To realize low-carbon advancements within energy systems, this study proposes a low-carbon economic scheduling framework for the IES that integrates the coupled operation of carbon-capture power plants, power-to-gas (CCPP–P2G) unit and the price-based demand response (PDR) considering dynamic carbon trading mechanisms. The proposed approach firstly concurrently considers source-side CCPP-P2G and load-side PDR to achieve coordinated low-carbon operation, elucidating the underlying mechanisms. Subsequently, a dynamic carbon trading model for the IES is formulated, flexibly capturing the ebb and flow of supply and demand in the carbon trading market. Amidst the uncertainty of the system, a bi-level optimization model is constructed to minimize the operational cost. Finally the effectiveness of the proposed method is demonstrated on an IES consisting of the IEEE-30 bus power system, 6-bus natural gas system and 6-bus district heating system. The findings indicate that the proposed dynamic carbon trading mechanism is able to effectively provide time-varying carbon trading price signals to fully reflect the supply and demand relationship of carbon emission allowance. Furthermore, the synchronized operation of CCPP-P2G and PDR yields substantial benefits, compared to the benchmark. Specifically for the test case, the proposed framework reduces system operational costs, wind power curtailment, and carbon emissions by $87,160, 1,410 MWh, and 4,849t, respectively.

Optimization Method of Photovoltaic Microgrid Energy Storage System Based on Price-based DR

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

A Dynamic Peer-to-Peer Electricity Market Model for a Community Microgrid With Price-Based Demand Response

Peer-to-Peer (P2P) energy sharing enables prosumers within a community microgrid to directly trade their local energy resources such as solar photovoltaic (PV) panels, small-scale wind turbines, electric vehicle battery storage among each other based on an agreed cost-sharing mechanism. This paper addresses the energy cost minimization problem associated with P2P energy sharing among smart homes which are connected in a residential community. The contribution of this paper is threefold. First, an effective Home Energy Management System (HEMS) is proposed for the smart homes equipped with local generation such as rooftop solar panels, storage and appliances to achieve the demand response (DR) objective. Second, this paper proposes a P2P pricing mechanism based on the dynamic supply-demand ratio and export-import retail prices ratio. This P2P model motivates individual customers to participate in energy trading and ensures that not a single household would be worse off. Finally, the performance of the proposed pricing mechanism, is compared with three popular P2P sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and bill sharing (BS) considering different types of peers equipped with solar panels, electric vehicle, and domestic energy storage system. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.

Demand Management for Peak-to-Average Ratio Minimization via Intraday Block Pricing

Price based demand response schemes may significantly improve power system efficiency. Additionally, it is desired that such schemes yield improved power operation, by reducing the peak consumption. This paper proposes the Intraday Block Pricing (IBP) scheme, aiming to promote effective demand response among consumers by charging their electricity usage based on intraday time-slots. To design the prices associated with the proposed scheme, we formulate a bilevel optimization problem that aims to minimize the Peak-to-Average Ratio (PAR) and simultaneously benefit the consumers and the utility company. The bilevel problem is converted into a single-level Mathematical Program with Equilibrium Constraints (MPEC). The resulting MPEC is non-convex and includes nonlinear constraints. Hence, to obtain a solution, it is relaxed into a Mixed Integer Linear Program by dealing with all nonlinearities. To evaluate the conservativeness of the proposed approach, a lower bound to the cost of the original bilevel problem is obtained. The applicability of the proposed scheme is demonstrated with simulations on various case studies, which exhibit a significant reduction in PAR and economic gains for the utility company and consumers. Moreover, simulation results show that the solutions of the original and relaxed problems are equivalent, demonstrating the effectiveness of the proposed solution approach. Further simulation results demonstrate significant advantages in the performance of the IBP scheme when compared to existing schemes in the literature.

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 scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Efficient Techniques for Residential Appliances Scheduling in Smart Homes for Energy Management Using Multiple Knapsack Problem

The evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively.

Stochastic electrical, thermal, cooling, water, and hydrogen management of integrated energy systems considering energy storage systems and demand response programs

Energy hub (EH) is a multi-energy system that combines several energy sources to increase energy supply efficiency, flexibility, and reliability. In this paper, a stochastic two-step multi-objective optimization method is proposed for multi-EH planning that focuses on economic, environmental, and security concerns. In order to meet the demands for electricity, heating, cooling, hydrogen, natural gas, and water, the proposed model includes several components, such as nonrenewable and renewable energy sources, cogeneration units, converters, and storage systems. Also, price-based demand response programs are implemented to provide demand-side flexibility for EHs. The proposed multi-objective framework investigates the total cost of the system, average reserve index (ARI), emissions of the system, and energy not supplied (ENS), simultaneously. EHs categorize their objectives into main and secondary based on priority. Since the total cost is so important, it has been recognized as the primary objective that is minimized in the first step. Also, the emissions, ENS, and ARI are the secondary objectives that are optimized in the second step simultaneously. The proposed model's efficiency is evaluated using an 18-bus test network in both summer and winter seasons. The simulation results present that the proposed model improves the emission, ARI, and ENS by 47.78 %, 53.4 %, and 23.38 % in summer seasons, respectively. And also, the multi-objective framework improves the emission, ARI, and ENS by 45.66 %, 65.84 %, and 52.56 % in winter seasons, respectively.

Demand Response Approach for Coordinated Scheduling of EV Charging in a Micro-Grid

Compared to traditional fuel vehicles, electric vehicles (EVs) can use energy efficiently without polluting the environment, which is why environmental experts are pushing the switch to EVs. When a large number of EVs are connected to the present power grid, negative impacts to the power system dynamics may happen. Implantation of demand response (DR) programming may avoid unplanned demand peaks, and support grid voltage. The objective of this article is to minimize the cost of charging the EVs while also utilizing their battery storage capacities to support grid voltage. Price-based DR programming is used to control the EVs’ charging. A study of an 8-bus distribution system with three EVs connected during charging/discharging periods, and different scenarios are simulated and compared with the grid response without any EV. New distribution tariffs have been developed to encourage small electricity users toward peak load restriction. The proposed scheduling method is valuable for the utility to estimate the capability of different control strategies for EV charging. It can also be extended to more complicated systems with large number of EVs and hence applied to a real network. MATLAB environment is used to test the suggested control scheme’s robustness and efficacy.

Real-time demand response strategy base on price and incentive considering multi-energy in smart grid: A bi-level optimization method

Real-time pricing (RTP) is an efficient approach for demand side management, which mainly aims at reducing peak-to-average ratio (PAR) and balancing power supply and demand. However, in real life, users’ real power consumption sometimes deviates significantly from the ordering power, which results in waste of power resources. Only the price-based demand response (FBDR) is difficult to tackle the deviation problem, especially in the multi-energy generation system. To address this issue, a two-stage hybrid demand response (DR) strategy, which combines RTP and real-time incentive, has been proposed in this paper to minimize the difference between the real power consumption and the ordering power. The first stage is to determine the optimal ordering power via RTP strategy, and then the power supplier and users reach an agreement of power ordering (APO). The second stage is to minimize the difference between the real power consumption and the ordering power via real-time incentive and APO. According to the random dynamic interaction between users and the power supplier, a bi-level model is constructed. Multi-energy generation and energy storage system (ESS) on the supply side, and photovoltaic (PV) generation on the demand side are also considered in this model. Considering the characteristics of the upper and lower objectives and the benefits of both supply and demand sides, the corresponding distributed optimization algorithm is designed to solve the model. Finally, the real-time price, optimal power consumption and optimal power supply are obtained. The simulation results show that the proposed DR strategy and algorithm not only fully guarantee the benefits of users and the power supplier, but also have a good performance in shaving peak and filling valley.

A distributive energy price-based hybrid demand response mechanism facilitating energy saving

Against the backdrop of deep concern over the negative externalities of fossil fuel energy use and the continued growth of distributed renewable energy, traditional demand response (DR) mechanisms need to be transformed and to include energy-saving objectives. This study proposes a distributed energy price-based hybrid DR (DEPB-HDR) mechanism that combines real-time incentives, tariffs, and distributive energy procurement prices. Under DEPB-HDR, the grid administrator provides optimal real-time incentives for power distributors (PDs) and sets the optimal real-time procurement prices for power prosumers (PPs) to satisfy the objectives of minimum total cost and thermal power generation. PDs determine their optimal real-time tariffs based on their incentives. PPs play a dual role: the demand side adjusts their electricity purchases according to real-time tariffs, and the supply side changes their power sales according to real-time procurement prices. The proposed mechanism is analysed using a three-layer Stackelberg game, and a distributed algorithm is designed to account for privacy and information incompleteness. Finally, numerical simulations were conducted, and the results showed that DEPB-HDR could increase energy savings while ensuring participants’ interests.

Risk management of energy communities with hydrogen production and storage technologies

The distributed integration of renewable energy sources plays a central role in the decarbonization of economies. In this regard, energy communities arise as a promising entity to coordinate groups of proactive consumers (prosumers) and incentivize investment on clean technologies. However, the uncertain nature of renewable energy generation, residential loads, and trading tariffs pose important challenges, both at the operational and economic levels. We study how this management can be directly undertaken by an arbitrageur that, making use of an adequate price-based demand response (real-time pricing) system, serves as an intermediary with the central electricity market to coordinate different types of prosumers under risk aversion. In particular, we consider a sequential futures and spot market where the aggregated shortage or excess of energy within the community can be traded. We aim to study the impact of new hydrogen production and storage technologies on community operation and risk management. These interactions are modeled as a game theoretical setting in the form of a stochastic two-stage bilevel optimization problem, which is later reformulated without approximation as a single-level mixed-integer linear problem (MILP). An extensive set of numerical experiments based on real data is performed to study the operation of the energy community under different technical and economical conditions. Results indicate that the optimal involvement in futures and spot markets is highly conditioned by the community’s risk aversion and self-sufficiency levels. Moreover, the external hydrogen market has a direct effect on the community’s internal price-tariff system, and depending on the market conditions, may worsen the utility of individual prosumers.

Two-stage electric vehicle charging optimization model considering dynamic virtual price-based demand response and a hierarchical non-cooperative game

This paper proposes a two-stage bi-layer game charging optimization model based on the background of non-coordination between a network operator (NO), a distributed generation operator (DGO), and a charging agent (CA). In the first stage, a dynamic virtual price-based demand response (DVPBDR) model is constructed to pre-optimize the charging load with the virtual charging cost as the objective. In the second stage, a strategy for adjusting output deviations based on a bi-layer Stackelberg game model is established, with the economic benefits of each participant as the objectives. Full cooperation mode and bi-layer mixed game are introduced to compare with the bi-layer Stackelberg game in Simulation Analysis. The calculation results show that: (1) the DVPBDR is not constrained by the actual electricity price system and mechanism, and reflect the real influence of price changes on charging demands, thus effectively reducing energy abandonment by 41.76% and net load fluctuation by 53.50%; (2) in the full cooperation mode, there is a conflict of interests and CA suffers financial loss, thus resulting in a reduction in comprehensive benefits by at least 61.08%, compared to the non-cooperative cases; (3) in the bi-layer mixed games, the cooperative gain of DGO and NO is superior than that of the cooperation between DGO and CA, so a relative win-win is achieved in the bi-layer mixed game with DGO-CA cooperation, and the comprehensive benefits is increased by 3.32%; (4) in the bi-layer Stackelberg game, each participant has a completely independent awareness of decision-making and establishes strategies for maximizing its own interests, which results in achieving the optimal comprehensive benefits (increases by at least 44.18% compared to other cases). Therefore, multi-dimensional benefits are realized in the multi-participant charging system with a bi-layer Stackelberg game.

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.

Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses

To explore the influencing factors of the integrated benefit (IB) index of integrated energy systems (IESs) and the best energy supply mode, we constructed a two-stage optimization model for planning allocation and operation scheduling. First, according to the structural characteristics of the energy demand load combined with the operation characteristics of each piece of equipment in the IES, a refined capacity allocation constraint model was constructed. Second, an IB optimization model with economic, environmental, and energy benefits as the optimization objectives was established. Then, by analogy with the price-based demand response (PBDR) and incentive-based demand response (IBDR) of the power system, an integrated demand response (IDR) model covering cooling, heating, electricity and gas was constructed. Finally, a citizen service center in Hebei Province, China, was used as a simulated example. The scientific capacity allocation of each device of the IES improved the economic efficiency by 14.8%. Compared with the traditional operation mode, the economic benefits of the multi-objective optimization (MOO) operation mode were increased by 2.97% and 3.42%, respectively. After further introducing the IDR, IB increased to 20.55 and 20.46%, and the effect of IBDR was better.

Optimal infrastructure in microgrids with diverse uncertainties based on demand response, renewable energy sources and two-stage parallel optimization algorithm

Future electric grids will face severe uncertainties with the unprecedented penetration of renewable energy sources, which may cause problems in grid exploitation. It is essential to evaluate the uncertainty of system performance in this grid; therefore, traditional exploitation methods may not be suitable for distributions grid such as multi-carrier microgrids when considering the electricity and gas grid constraints. This paper proposes an effective method for simultaneously optimizing different types of energy infrastructure in an environment with various uncertainties while considering grid constraints. To address the multi-energy synergy supply optimization problem in the micro energy grid, a coordinated two-stage programming approach is also suggested. A day-ahead and real-time phases are separated in the scheduling cycle to overcome the effects of the unpredictability of wind energy and solar power. The findings of the day-ahead forecast are then taken into account as uncertain variables for the higher-layer model. The demand response programming model and the energy storage revision model are lower-layer models considering the actual value as the realization of uncertain variables. The competitive swarm optimization algorithm is used to solve the problem mentioned above The Cumulative search optimization (CSO) uses global and local systems in particle swarm optimization and adopts a novel learning mechanism for creating competition between particle pairs. Comprehensive numeric examinations show that convergence speed and precision are critical, especially in solving large-scale problems. Therefore, we use chaos theory to improve its performance. Finally, the proposed method is tested and discussed on a system. The results showed that: (1) power-to-gas could convert extra electricity into natural gas; (2) the proposed two-stage optimization model and algorithm achieved different energy optimizations; (3) price-based demand response (DR) can level the energy load curve and maximize multi energy grid (MEG) revenue by using complementary specifications of energy price; and (4) the micro energy grid could communicate with the high-degree energy grid in order to gain more economic benefits. Accordingly, the incentive-based demand response (IBDR) output is reduced. The electricity and heating provided by convention gas turbine (CGT) is increased, which is fed to upper power grid (UPG) and upper heating grid (UHG). Since MEG’s purchasing power from UPG during the peak period generates more total revenue than island operations. Based on the obtained results, it can be seen that the proposed algorithm was about 50% faster compared to other methods and its standard deviation was about 0.0013 with different implementations, which was much better compared to other models.

An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG

The popularization of combined cooling, heating and power microgrid containing renewable energy sources (RES-CCHP MG) can effectively alleviate the energy crisis and reduce the emission of air pollutants. However, uncertainties in renewable energy generation and load negatively affect the operation of microgrid. To solve this problem, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. First, the price-based demand response is introduced to establish the RES-CCHP MG model that is more in line with practical application requirements. In the day-ahead dispatching stage, robust adjustment coefficients are introduced to improve the defects of traditional robust optimization which is conservative, and a two-layer robust optimization model is established to improve the ability to resist risks. In the intra-day dispatching stage, in order to reduce the impact of day-ahead prediction error and real-time power fluctuation, the hierarchical rolling optimization model based on model prediction control is established to face the situation that different loads have different response speeds. The simulation results show that compared with the traditional optimal dispatching strategy, the operating cost and the peak-to-valley difference of electric load are reduced by 5.4% and 14.7%, respectively, and the utilization of renewable energy is increased by 4.8%.

Corrigendum: Day-ahead economic dispatch strategy for distribution network considering total cost price-based demand response

Corrigendum: Day-ahead economic dispatch strategy for distribution network considering total cost price-based demand response

Ultra Short-term Load Optimal Dispatching Method for Micro Grid Considering Demand Side Response

With the development of the economy, the electricity demand is also increasing. However, the traditional micropower supply bears the problems of excessive load growth and unstable voltage. Therefore, this paper takes power generation as the research object. First, the principle of output optimization based on schedulability, reserve capacity, and intermittent operation frequency of units is analyzed. Then, the relationship between the optimal solution and the minimum power consumption time is calculated by establishing a mathematical model to solve the algorithm, and the difference between the two results is compared. Finally, the power system is simulated on Matlab software, and the method and effect are verified by numerical simulation. The simulation results show that with the same microgrid power configuration, there is a 3.5% load transfer at time-sharing prices and a 6.4% load transfer at real-time prices through price-based demand response. The optimization objective of demand response, namely the cumulative sum of the difference between new energy power generation and load, is 15312 kW · h per year when the fixed price is implemented, 54134 kW · h is for the time of use price, and 451645 kW · h is for real-time price. The response effect of real-time price is optimal.