Demand Response Potential Research Articles

The output flexibility optimization control of load-side wind turbine considering client demand response

Abstract This study focuses on optimizing the output flexibility control of load-side wind turbines considering client demand response. Considering the potential and role of client demand response, a load-side wind turbine output objective function was constructed, client demand response constraints were designed, and a two-layer model for flexible optimization control of load-side wind turbine output was constructed. The top is the power allocation hierarchy, and the core objective of this layer is to achieve power optimization distribution of wind turbines under various constraint conditions. The lower layer is the frequency control layer, which optimizes and controls the dynamic frequency of load-side wind turbine output. The experimental results show that the optimized control strategy not only enhances the efficiency of wind turbines, but also effectively reduces energy loss and enhances the stability and reliability of the power grid. Through precise calculation and real-time adjustment of the double-layer model, we can more accurately predict and control the output of wind turbines, thereby ensuring the supply-demand balance of the power grid and reducing potential risks caused by frequency fluctuations and power offsets.

Demand Response Potential of an Educational Building Heated by a Hybrid Ground Source Heat Pump System

Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.

A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.

Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storage

The surge in air conditioning electricity consumption exacerbates grid peak load. To counteract grid peaking pressures and accommodate a high penetration rate of renewable energy, a photovoltaic direct-driven air-conditioning system (PVACS) integrated with energy storage was suggested. The power response characteristics of the air conditioner based on indoor temperature set-point regulation were clarified with an on-site test. The test results indicated a significant flexible energy utilization potential through intelligent temperature management. Whereas, its flexibility is limited by high ambient temperature or inadequate air conditioner capacity. Then, the effects of demand response were simulated, aiming to achieve the synchronization between air conditioner power and photovoltaic (PV) power. Simulation results showed that the demand response strategies effectively reduced peak power by 45.2% and increased self sufficiency ratio and self consumption ratio to 0.83 and 0.91, respectively. Moreover, the role of short-term electricity energy storage was considered for further promoting solar energy accommodation and grid peak shaving. Optimized energy storage further reduced peak power by more than 23.4%, while mitigating the temporal supple-demand mismatch.

Characterization and prediction of demand response potential of air conditioning systems integrated with chilled water storage

Chilled water storage offers a cost-effective and convenient solution for load flexibility of air-conditioning systems. However, its impacts on system flexibility and energy efficiency have not been comprehensively explored. In this paper, demand response (DR) potential delivered by chilled water storage was explored and predicted through both experimental and simulation studies. The initial experimental investigation focused on the DR potential at various cold charging and discharging proportions of chilled water storage. Subsequently, a virtual platform, validated through experimental data, was utilized to analyze load flexibility and energy efficiency of the system across different baseline loads, storage characteristics, and heat pump characteristics. Prediction models for DR potential in different cold charging and discharging scenarios were further developed based on the simulation results. The experimental and simulation results demonstrated effective load manage performance through adjusting charging and discharging proportions, even though the flexibility may sacrifice energy efficiency in cold discharging scenarios. In addition, the developed prediction models confirmed their feasibility with the coefficient of determination (R2) exceeding 0.80 in charging scenarios and 0.90 in discharging scenarios. The analysis also revealed significant benefits from storing supply water at lower temperatures and selecting heat pumps with partial-load efficiency curves characterized by higher Pearson Correlation Coefficients.

Optimizing peak-shaving cooperation among electric vehicle charging stations: A two-tier optimal dispatch strategy considering load demand response potential

The increase in the grid connection of electric vehicles (EVs) provides great potential for peak load regulation and valley filling of the grid. In order to solve the challenges brought by the integration of new energy vehicles into the power grid and give full play to the potential of EV demand response, this paper proposes a two-layer optimal dispatch strategy for the “distribution network-charging station” system. First, assess EV load demand response potential. The research area is divided into different functional areas by using the data of urban interest points, and the travel habits of users are analyzed by using EV driving data. On this basis, considering the influencing factors such as EV battery capacity (SOC), charging electricity price and spatial characteristics, the EV load response potential evaluation model is constructed by integrating user electricity price response and charging power response. Secondly, taking the evaluation value of EV response potential as the range of load adjustment, in order to optimizing peak-shaving cooperation among EV charging stations and obtaining the peak-shaving measures of each charging station, a “distribution network-charging station” double-layer optimal dispatch is carried out. The upper-level distribution network scheduling aims at minimizing the unfinished peak shaving rate and distribution network loss, and optimizes the scheduling power allocation of the peak tasks of each charging station. The lower-level charging station scheduling is based on the principle of maximizing user charging satisfaction and charging station economic benefits, and completes the peak-shaving scheduling determined by the superior. Finally, the strategy is applied in a specific area of Shaanxi Province, and the scheduling results show that the strategy is more economically feasible.

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.

Research on analysing demand response potential of electricity users based on ISODATA-GMM

Research on analysing demand response potential of electricity users based on ISODATA-GMM

Comparison of different control methods on the thermally activated building system (TABS) with large energy flexibility

Thermally Activated Building Systems (TABS) has great energy flexibility potential for the effective demand response, but the inherent large thermal inertia creates challenges for the control. Model Predictive Control (MPC) can improve the control efficiency while achieving certain objectives. However, the modeling mechanism of the TABS thermal behavior can lead to the difference in the controller performances. In this study, the white-box model, grey-box model, and black-box model based MPCs (referred to as W-MPC, G-MPC, and B-MPC) are developed and analyzed in depth for a case TABS with high heat flexibility potential. The performances of MPC strategies are investigated together with the baseline Rule Based Control (RBC) strategy under normal conditions as well as a variety of uncertainty events. In general, MPC strategies show better performance compared to the RBC. The prediction-based control can effectively maintain the indoor temperature within the constraints considering the uncertain disturbances and the gradual outdoor climate change. Moreover, MPC strategies help to increase the cost savings due to the effective load shifting by 30%–50%, and enhance the flexible energy utilization by 14%–29%. In terms of the comparison among the different MPC strategies, W-MPC shows better performance in the room temperature control, which reduces the violation of the indoor temperature constraint by 30% and 15% compared to G-MPC and B-MPC, respectively. G-MPC shows slight superiority concerning the economy and flexible energy usage. The normalized cost saving of G-MPC is approximately 3% and 8% lower compared to the W-MPC and B-MPC, respectively, while the utilization efficiencies of the flexible energy are approximately 6% and 12% higher.

A Method for Calculating the Environmental Protection Cost of Cement Plants Taking into Account the Diffusion Characteristics of Air Pollutants

Abstract In recent years, China’s installed capacity of new energy has been continuously improved, and the problem of new energy consumption has become increasingly prominent. The cement load has considerable demand response potential and can effectively help absorb new energy sources, but its high emission characteristics limit the development of the cement industry. In this context, the establishment of a systematic and refined cement load emission cost model will be of great significance to improve the wind power absorption capacity and promote the low-carbon and environmentally friendly operation of the power system. In this paper, the emission characteristics of SO2, NOx, particulate matter and CO2 from the cement load are analyzed. according to the cement production process. Then, the air pollutant emission cost of the cement plant is described from the aspects of environmental protection tax and environmental protection supplementary cost, and the carbon emission cost of the cement plant is calculated by using the laddered carbon transaction cost model, and finally the emission cost model of the cement plant is constructed.

The Cost Reduction Potential of Demand Response in Balancing Markets from a System Perspective

Demand response (DR) can potentially provide a cost-efficient alternative for balancing the electricity grid by replacing fossil-fuelled power plants for the provision of flexible capacity. This paper aims to quantify the cost reduction potential of DR from a system perspective. Historical data of balancing markets are studied using regression and average bid price analysis to quantify the effect of the participation of DR resources on the price of flexible capacity for the provision of balancing reserves by focusing on two case studies in Great Britain and the Netherlands. It is estimated that DR bids are, on average, 35% lower than the market average. The regression analysis concluded that 1% higher participation of DR in balancing markets leads, on average, to a 2.7% lower prices for flexible capacity. The results verify the hypothesis that flexible DR capacity is offered at a lower price on balancing markets compared to conventional generation resources, resulting in lower costs for grid operators to balance the grid, thus reducing societal costs for electricity provision and overall emissions through the integration of low-carbon balancing resources.

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.

Direct load control-based optimal scheduling strategy for demand response of air-conditioning systems in rural building complex

The abrupt surge in electricity consumption for air-conditioning (AC) in rural areas has exacerbated the rural power supply and demand imbalance, presenting a significant challenge to the reliability and stability of power provision in rural low-voltage grids. Implementing power demand response (DR) in buildings is recognized as an effective solution to address the grid's power imbalance and reliability issues. Among various building DR strategies, direct load control (DLC) stands out as a proficient approach to rapidly curtail building AC loads. However, applying this method solely to individual rural buildings proves less effective in peak regulation (PR). To fully exploit the DR potential of AC systems in rural building complex, it is essential to scale up the application of DLC. Consequently, a DLC-based DR optimal scheduling strategy is proposed for rural building complex AC systems. This strategy aims to meet PR demand and maximize the benefits for load aggregator. It establishes four DLC methods and differential compensation mechanisms, taking into account the varying thermal comfort requirements of AC users. The DR optimization model is solved using a genetic algorithm (GA), with PR demand derived from long and short-term memory neural network predictions. To validate the proposed strategy, a simulation study is conducted on a 200-unit farmhouse AC system, utilizing a typical rural soil source heat pump system in Xi'an, Shaanxi Province. The results demonstrate that the strategy surpasses the PR target by approximately 6.65 % during the DR period, while ensuring a minimum level of thermal comfort for all user categories. Additionally, it yields a PR benefit of 88.73 yuan per day for all users, thereby promoting the adoption of clean energy in rural areas.

Integrated Demand Response for Micro-Energy Grid Accounting for Dispatchable Loads

Micro-energy networks are the smallest element of integrated energy systems, and tapping into the integrated demand response potential of micro-energy networks is conducive to improving energy use efficiency and promoting the development of new energy sources on a large scale. This paper proposes a day-ahead integrated demand response strategy for micro-energy grid that takes into account the dispatchable loads. Considering the gradient use of thermal energy, a typical micro-energy grid structure including electricity, gas, medium-grade heat, low-grade heat, and cold energy is constructed, a comprehensive energy equipment model is established, and the refined scheduling models of the dispatchable loads are given. On this basis, with the operating economy of the micro-energy grid as the optimization objective, the integrated demand response strategies of tariff-type and incentive-type are proposed. Through case study analysis, it is verified that the proposed strategy can optimize the energy consumption structure of the micro-energy grid under the guidance of time-of-use tariffs, reducing the operating costs. The proposed strategy fully exploits the demand response potential of the micro-energy grid through the dispatchable loads and the multi-energy complementarity of electricity, heat, and cold, realizes the comprehensive coordination and optimization of source-network-load-storage, provides a larger peak-regulating capacity, and exhibits practical applicability in engineering.

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Developing flexible resources is a key strategy for advancing the development of new power systems and addressing the issue of climate change. Demand response is a crucial flexibility resource that is extensively employed due to its sustainability and economy. This work develops a three-stage demand response potential evaluation model based on “theoretical potential–realizable potential–multi-load aggregation potential” in response to the issues of inadequate consideration of numerous complicated agents and time in previous research. Firstly, the traditional method calculates the theoretical maximum demand response potential of a single industry in each period. Based on this, the industry characteristics are taken into account when establishing the demand response potential evaluation model. Lastly, the time variation of the demand response potential is taken into consideration when evaluating the demand response potential of multiple load aggregation. For the analysis, three industries are chosen as examples. The results show that the potential of peak shaving and valley filling obtained by using the model is smaller than that of the traditional method, the reduction range of peak cutting demand response potential calculated by multi-load aggregation is 19–100%, and the reduction range of valley filling demand response potential is 20–89%. The results are closer to reality, which is conducive to improving the accuracy of relevant departments in making relevant decisions and promoting the sustainable development of a new power system.

Multi-objective optimization based demand response program with network aware peer-to-peer energy sharing

Demand-side energy management programs, such as Demand Response (DR) and Peer-to-Peer (P2P) energy sharing, have recently begun to be implemented to optimize the integration of distributed clean energy resources and to leverage generation and demand-side flexibility in the distribution system. In general, if not optimally planned, P2P energy sharing may have a negative impact on power distribution utilities’ primary objectives, such as minimizing energy losses, flattening the load profile, and improving voltage regulation. The DR program is considered as one of the potential mechanisms to alleviate the possible adverse impact of P2P energy sharing while maintaining the interest of peers participating in decentralized energy sharing. The analysis of the impact of P2P energy sharing with varying peers’ physical locations involved utilizing the Voltage Sensitivity Factor (VSF). The influence on the distribution network is contingent upon the difference in VSF values among the individual peers engaged in energy sharing. This study introduced the k-Means algorithm for VSF-based peer clustering, thereby generating the merit order and realizing the network-aware P2P energy transactions for the selected scenarios. A multi-objective optimization model-based real-time pricing-based DR scheme has been formulated to address the adverse impact of P2P energy sharing. Furthermore, this study quantifies the impact of DR potentials, which vary from 5% to 45% in increments of 10%, on the Peak-to-Average Ratio (PAR), overall energy loss, and total energy cost reduction. The modified IEEE 33 bus system incorporating industrial, commercial, and residential consumers’ load profile of 24 h at selected buses has been considered to test and validate the proposition.

A Survey of Commercial and Industrial Demand Response Flexibility with Energy Storage Systems and Renewable Energy

The transition from traditional fuel-dependent energy systems to renewable energy-based systems has been extensively embraced worldwide. Demand-side flexibility is essential to support the power grid with carbon-free generation (e.g., solar, wind.) in an intermittent nature. As extensive energy consumers, commercial and industrial (C&I) consumers can play a key role by extending their flexibility and participating in demand response. Onsite renewable generation by consumers can reduce the consumption from the grid, while energy storage systems (ESSs) can support variable generation and shift demand by storing energy for later use. Both technologies can increase the flexibility and benefit by integrating with the demand response. However, a lack of knowledge about the applicability of increasing flexibility hinders the active participation of C&I consumers in demand response programs. This survey paper provides an overview of demand response and energy storage systems in this context following a methodology of a step-by-step literature review covering the period from 2013 to 2023. The literature review focuses on the application of energy storage systems and onsite renewable generation integrated with demand response for C&I consumers and is presented with an extensive analysis. This survey also examines the demand response participation and potential of wastewater treatment plants. The extended research on the wastewater treatment plant identifies the potential opportunities of coupling biogas with PV, extracting the thermal energy and onsite hydrogen production. Finally, the survey analysis is summarised, followed by critical recommendations for future research.

Assessment of Electricity Demand Response Potential under Consideration of Uncertainties

Abstract The assessment of customer electricity demand potential is a critical part of further stimulating electricity demand growth and promoting utility development. The main aim of this paper is to investigate how electricity demand response potential can be assessed under uncertainty. The decision variables are set through a two-stage stochastic planning model, focusing on fuzzy decision-making through linear programming in interval planning, combining with consequent opportunity constraint planning and fuzzy opportunity constraint planning to realize artificial regulation of unfavorable factors, and finally completing the treatment of uncertain factors. Combining a Gaussian mixture model to construct a model for measuring electricity demand response potential. It has been found that different electricity pricing policies lead to other potential demand response values for peak electricity load. The optimal electricity pricing and revenue of each user will be greatly affected by the participation of different users in the electricity demand response. The optimal power pricing decreases from 3-4 when the number of users is 4 to 2.5 when the number of users is 20 because of the generation of scale rewards, and the benefits to the users increase substantially when the number of users participating in electricity demand response is 20. Evaluating customer electricity demand in an uncertain environment can help drive electricity system reform to some extent.

A linear reduced-order model for the activated sludge process for the integration into a mixed-integer linear energy system optimisation model

Conventional wastewater treatment plants consume significant amounts of electricity. The constant aeration of the wastewater in order to foster the growth of microorganisms or the pumping of wastewater are two examples for energy-intensive processes within a plant. Case studies have shown that switching off blowers and inlet pumps for a certain period of time is possible without a loss in water quality. This yields a potential for wastewater treatment plants to provide demand response (DR) to the power system and thereby increase overall system flexibility. So far, the DR potential has only been quantified for individual plants, while the effects of large-scale DR provision by the wastewater treatment sector for the power system have not yet been studied. One reason for this is the lack of optimisation models which include both the wastewater treatment process and the power system operation in sufficient detail. Our model tackles this gap in the literature by providing a reduced-order linear biochemical model for the activated sludge process within a WWTP that can be incorporated into an operational energy system model. The results show that the effluent concentrations are predicted well by the linear reduced-order model in comparison to the results of the Standard Activated-Sludge model No. 1 (ASM1). Potential model applications are the variation of the airflow rate within a certain range and the variation of liquid influent flow rate to the system, which is a result of electricity load shedding of the inlet pumps and the blowers connected to the activated sludge tank.

Physics-based modeling of electricity load profile of commercial building stock considering building system composition and occupancy profile

Physics-based building stock energy models (BSEMs), robust support tools for policymaking, play an important role in modeling time sequence electricity load profiles of the commercial sector. However, the conventional modeling approach, assisted by physics-based BSEM, inadequately considers the occupancy profiles of large populations and the heterogeneity of building systems, which could result in significant errors. To address this issue, this study proposes a novel framework of physics-based BSEM for modeling electricity load profiles in the commercial sector. The proposed framework incorporated both occupancy modeling, using travel survey data on daily travel patterns of the population, and stock modeling, to consider the heterogeneity of building systems within the physics-based BSEM. The methodology provided that (1) the reproducibility of occupancy presence schedules and electricity load profiles can be enhanced by the occupancy modeling; (2) building system stock modeling can improve the estimated electricity load profile. Moreover, the suggested method offers analytical capabilities for forecasting long-term variations in electricity load profiles and estimating the demand response potential of commercial building stocks.