Comprehensive Demand Response Research Articles

Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory

To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty.

A demand response method for an active thermal energy storage air-conditioning system using improved transactive control: On-site experiments

Transactive control (TC) and active thermal energy storage (ATES) strategies can effectively achieve a supply–demand balance across energy sources in the power grid. However, past research mainly focused on one of these demand response (DR) strategies, and integrated DR strategies that combine TC and ATES are unavailable. Therefore, to fully utilize both TC and ATES strategies in the DR of air-conditioning systems and thus enhance power grid stability, a comprehensive DR strategy that combines an improved version of TC with ATES (ITC + ATES) was developed for energy storage air-conditioning systems. The ITC + ATES strategy utilizes the automatic feedback regulation effect of the real-time electricity price on the grid side to the air-conditioning users. Then, the system bids the cooling or heating demand on the user side in the power market, thus adjusting the dynamic temperature set-point in real time. The ATES system's automatic energy storage and release process is then regulated accordingly. Thus, this strategy not only allows the air-conditioning system to participate in the power DR, but ensures that the electricity consumption of the air-conditioning system conforms to the power market law and realizes the optimal social value of the power commodity. On-site test experiments of space cooling and heating conditions were conducted on a full-scale intelligent centralized air-conditioning experimental platform with an ATES system to test the effect of this strategy. Comparing the ITC + ATES strategy with the conventional control strategy, ATES strategy, and ITC strategy reveals that the ITC + ATES strategy is most effective in ensuring the thermal comfort of occupants, particularly during the DR period. Moreover, the maximum total electricity consumption reduction rate, the maximum total operating cost reduction rate, the maximum electricity consumption reduction rate during the DR period, and the maximum total operating cost reduction rate during the DR period of the ITC + ATES strategy were 33.20%, 49.69%, 76.14%, and 76.16%, respectively. These results suggest that the ITC + ATES strategy is an efficient and integrated DR approach.

Research on optimal scheduling of integrated energy system in low-carbon parks based on demand response

Abstract The comprehensive energy system of the park is one of the effective ways to solve the problems of low utilization efficiency of comprehensive energy and difficulty in absorbing renewable energy. By coordinating the output of each unit to optimize the scheduling of the system, the operating cost of the system can be reduced to a certain extent, and the space for new energy generation to be connected to the Internet can be increased. Aiming at the characteristics of electric, heat and gas load demand of the integrated energy system of industrial low-carbon parks and the overall needs of low-carbon development, a research on the optimal scheduling of integrated energy systems in low-carbon parks based on demand response is proposed. By analyzing the structure and components of the integrated energy system of the low-carbon park, different types of demand response are modeled. On this basis, based on the comprehensive demand response of electricity and heat, the optimal dispatch model of the integrated energy system of the low-carbon park is constructed, and the model is solved to realize the optimal dispatch of the integrated energy system of the low-carbon park. The experimental resu lts show that the proposed method has lower operating cost of the integrated energy system, better economy of the integrated energy system and can effectively improve the power supply reliability and energy saving rate of the integrated energy system. The average system load rate of the proposed method is up to 98.7%, the average comprehensive energy utilization rate is up to 97.9% and the system operation cost is only 10343.1 yuan.

The Planning Method of Township Regional Low-carbon Energy System Based on Demand Side Response

To solve the problem of environmental pollution caused by rising energy demand and explore the impact of demand-side resources on system optimal scheduling, based on the EH model, it is of great significance to study the integrated demand response strategy, carbon trading mechanism and low-carbon energy uncertainty for the operation of IES low-carbon economy. Compared with the traditional load response model, this model has better flexibility with the adjustment response. According to the thermal inertia and certain time delay characteristics of the heat load, the demand response model of heat load is established. Then, according to the application of the multienergy price demand response theory in natural gas system, the optimization model of gas load response is constructed. Three different optimization scenarios are set for simulation and comparative analysis, which verifies that the township comprehensive demand response model considering electric and thermal gas load has a significant effect on improving the system economy and the absorption capacity of low-carbon energy.

Low-Carbon Economic Dispatching of Multi-Energy Virtual Power Plant with Carbon Capture Unit Considering Uncertainty and Carbon Market

Multi-energy virtual power plants (MEVPPs) effectively realize multi-energy coupling. Low-carbon transformation of coal-fired units at the source side and consideration of demand response resources at the load side are important ways to achieve carbon peak and carbon neutralization. Based on this, this paper proposes a low-carbon economic dispatch model for the MEVPP system considering source-load coordination with comprehensive demand response. Combined with the characteristics of organic Rankine cycle (ORC) waste heat power generation and comprehensive demand response energy to increase the flexibility on both sides of the source and load, the problem of insufficient carbon capture during the peak load period in the process of low-carbon transformation of thermal power units has been improved. First, the ORC waste heat recovery device is introduced into the MEVPP system to decouple the cogeneration unit’s “heat-based electricity” constraint, which improves the flexibility of the unit’s power output. Secondly, we consider the synergistic effect of the comprehensive demand response and ORC waste heat recovery device and analyze the source-load coordination low-carbon dispatch mechanism. Finally, an example simulation is carried out in a typical system. The simulation example shows that this method effectively improves the carbon capture level of carbon capture power plants, takes into account the economy and low carbon of the system, and can provide a reference for the low-carbon economic dispatch of the MEVPP system.

Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties

The volatility of wind power and the uncertainty of demand response have brought greater challenges to the operation of integrated electricity and natural gas systems. Therefore, an optimal operation model considering comprehensive demand response uncertainty is proposed. First, chance-constrained is used to deal with the uncertainty of wind power. The uncertainty of three demand response loads that can be transferred, reduced, and replaced are modeled respectively. Secondly, considering that the two systems are different stakeholders, a distributed optimization model is established, the mixed-integer nonlinear model established by incremental linearization and second-order cone relaxation processing is further used, and the distributed optimization model is realized through target cascade (ATC). Finally, a case study of the integrated electricity and natural gas system composed of an IEEE 33-bus power system and 24-node natural gas system verifies that: 1) considering the demand response will reduce the system cost by 3.57%; 2) the necessity of considering the uncertainty of demand response; 3) the convergence of this method is better than ADMM method.

Community decision-makers’ choice of multi-objective scheduling strategy for integrated energy considering multiple uncertainties and demand response

• Construct the RE output uncertainty model by the probability box theory. • Use complex networks to classify the community residents. • A CDR model is built to mobilize various users to join in IESs scheduling. • Propose a utility model for decision-makers based on the S-shaped utility function. • Select the second optimal strategy by Muirhead mean operator. To address the impact of uncertainty on scheduling strategy in community integrated energy systems, a multi-objective optimal scheduling model under the constraint of various uncertainties and demand response is suggested. Firstly, a source-load uncertainty model and a comprehensive demand response model are built. Secondly, construct a multi-objective satisfaction model and utility model based on the energy supplier profit, resident cost, carbon treatment amount, and renewable energy utilization rate. Then, the best strategy is determined using the entropy weight method and the Muirhead mean operator. Finally, a multi-scene case of a residential area is used to validate the model's effectiveness. The results show that: 1) The source-load uncertainty model improves overall robustness. 2) The resident cost is reduced by 7.59%-9.84%, the carbon treatment amount is reduced by 17.71%-95.635%, and the energy supplier profit and the renewable energy utilization rate are increased. 3) The strategy with the best satisfaction or the best utility may not be the best choice among the objective functions of different relationships, so decision-makers need to choose according to the actual situation. The model is beneficial to the economy and environment, and it serves as a guide for community decision-makers in selecting a comprehensive energy multi-objective scheduling strategy.

RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm

The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of optimization, resulting in a low-quality scheduling scheme. Aiming at the dispatchability of electric and heat loads, this paper proposes an electric and heat comprehensive demand response model considering the participation of dispatchers. On the basis of incentive demand response, the group aggregation model of electrical load is constructed, and the electric load response model is constructed with the goal of minimizing the deviation between the dispatch signal and the load group aggregation characteristic model. Then, a heat load scheduling model is constructed according to the ambiguity of the human body's perception of temperature. On the basis of traditional GWO, the Fuzzy C-means (FCM) clustering algorithm is used to group wolves, which increases the diversity of the population, uses the Harris Hawk Optimizer (HHO) to design the prey to search for the best escape position, and reduces the local The optimal probability, and the use of Particle Swarm Optimizer (PSO) and Bat Optimizer (BO) to design the moving modes of different positions, increase the ability to find the global optimum, so as to obtain an Improved Gray Wolf Optimizer (IGWO), and then efficiently solve the model. IGWO can improve the defect of insufficient population diversity in the later stage of evolution, so that the population diversity can be better maintained during the entire evolution process. While taking into account the speed of optimization, it improves the algorithm's ability to jump out of the local optimum and realizes continuous deep search. Compared with the traditional intelligent Optimizer, IGWO has obvious improvement and achieved better results. At the same time, the comprehensive demand response that considers the dispatcher's desired signal improves the accommodation of new energy and reduces the operating cost of the system, and promotes the benign interaction between the source and the load.

A Collaborative Optimization Model for Integrated Energy System Considering Multi-Load Demand Response

The integrated demand response strategy participates in the coordinated operation of the integrated energy system, which can effectively improve the flexibility and stability of the system operation. This paper adopts a multiple load demand response strategy to guide users’ energy consumption habits. Firstly, the cooperative operation structure of integrated energy system considering comprehensive demand response is designed by analyzing the characteristics of multiple loads. Secondly, according to the interactive relationship between the output exchange power and the demand response adjustment of units, a two-stage collaborative optimization model is established. Finally, results show that considering the demand response of electricity-heat-gas load requires higher output power flexibility of the generators and enhances the ability of the system to participate in the demand response. The overall economic benefit of the system can be improved, but the comprehensive satisfaction of users will be reduced.

Demand response for manufacturing systems considering the implications of fast-charging battery powered material handling equipment

Electricity demand response is proved to be a viable approach to address the peak demand, improve the power grid reliability, and facilitate the grid integration of renewable resources. The implementation of demand response management in the manufacturing sector, which is one of the main energy consumers, has received significant attention. The existing studies in this field have traditionally focused on maximizing the benefits of demand response through machine control and scheduling. However, due to the widespread adoption of fast-charging infrastructure for material handling equipment (MHE), the high-power demand of fast charging and the close coordination between the machines and MHE pose much-increased complexity in the energy management, which necessitates a more comprehensive demand response scheduling method for the integrated manufacturing and material handling system. In this study, an analytical manufacturing and material handling system model is proposed to obtain cost-effective production schedules under demand response. The interactions between machines and MHE, production throughput requirement, battery charging characteristic, and time-varying electricity pricing are jointly considered in the integrated model to help manufacturers reap substantial benefits of demand response. The effectiveness of the proposed method is validated through numerical case studies, where the results indicate a 15.1% energy cost reduction compared to the benchmark scheduling scheme.

Operational optimization of integrated energy systems considering demand-side flexibility

In order to solve the operation optimization problem of integrated energy system considering the demand side, this paper takes the integrated energy system of industrial park as the research object and proposes an optimal scheduling model considering the comprehensive demand response. Algorithm analysis shows that RIES containing multiple energy storage devices fully consider the comprehensive demand side response.

A ‘spatial‐temporal’ bi‐layer optimal control strategy of large‐scale electric vehicles (EVs) in a multi‐area Virtual Power Plant (VPP) oriented to dual carbon target

Virtual power plant (VPP) is a comprehensive demand response (DR) technology to aggregate various power supply and consumption resources to help stabilise power grid fluctuations and reduce CO2 emissions. Fast-charging electric vehicles (EVs) with higher charging power and shorter charging time are the most potential high-quality DR resources of VPP in the future. In this paper, a new ‘spatial-temporal’ bi-layer optimal control strategy is proposed to solve the daily real-time DR dispatching problem of large-scale EVs including fast-charging ones in a multi-area VPP oriented to China's dual carbon target. This proposed bi-layer strategy has two main contributions in both spatial and temporal domains. One is that, compared with the limited geographical area in traditional VPP, the upper layer of the proposed strategy considers the DR problem of EVs in a VPP with multiple geographical areas and establishes a spatial optimisation model of EVs DR considering both energy and carbon transaction costs of the overall VPP. The other is that rather than traditional slow-charging EVs DR control with 1-h temporal interval, the lower layer of the proposed strategy adds fast-charging EVs in the real-time orderly charging and discharging DR control by a shorter temporal interval of 15 min and designs two different charging and discharging price stimulation scenarios. The simulation results in the last section of this paper show that the proposed ‘spatial-temporal’ bi-layer optimal control strategy can effectively guide the charging and discharging behaviours of EVs in both spatial and temporal domains of a multi-area VPP, achieving lower energy and carbon transaction cost and smaller load fluctuations.

Research on Cooperative Optimization Control Strategy of Multitype Energy Sources

With the increasing consumption of energy, the efficiency of energy utilization urgently needs to be further improved. At present, the coordinated optimization control of the integrated energy system of multiple types of cooling, heating, and power equipment is an important way to improve the comprehensive energy efficiency of the regional power grid and reduce the operating cost of the power grid. Aiming at this scenario, this paper establishes a fine energy storage model by analyzing the uncertainty of wind power output and considering the influence of low temperature and other conditions on the energy storage device in the energy storage side. On the load side, the influence of comprehensive demand response of electricity and heat on system operation is analyzed, and a combined cold, heat, and electricity supply system including renewable energy and energy storage device is established. Aiming at the optimal total cost of the integrated energy cooling and heating triple power system collaborative optimization control, the crow search algorithm is used to iteratively optimize the configuration model of the triple power system. The four models are considered in the article include groundless heat pump and energy storage, excluding joint demand response, including ground source heat pump and traditional energy storage model, excluding joint demand response, including ground source heat pump and fine energy storage model, excluding joint demand response, including ground source heat pump and fine energy storage model, taking into account the joint demand response. The simulation results show that the cooperative optimization control strategy of the combined cooling, heating, and power system with renewable energy and fine energy storage device model can enhance the system’s schedulable space, improve the comprehensive energy utilization efficiency, and have considerable economic benefits.

Demand response program for smart grid through real time pricing and home energy management system

Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.

Three-level market optimization model of virtual power plant with carbon capture equipment considering copula–CVaR theory

To achieve carbon neutrality, promoting clean and renewable energy consumption and reducing CO2 emissions have become important measures. Therefore, virtual power plants (VPPs) containing carbon capture devices have become a research focus. This study first builds a VPP model containing carbon capture devices, analyzes the cooperative operation mode of a carbon capture system and power-to-gas, and develops a comprehensive demand response mechanism for the VPP. Second, owing to the uncertainty of the electricity market clearing price and the natural gas market price, a risk dependence model between electricity price and gas price based on the copula–conditional value-at-risk theory is constructed. Then, a VPP three-level market optimization model considering risk dependence and carbon trading mechanisms is proposed, and an improved collaborative evolutionary algorithm that combines the NSGAII and AGE-MOEA algorithms is used to solve multi-objective problems. Finally, an example of a VPP is used to verify the effectiveness of the model.

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

Planning and Scheduling for Industrial Demand-Side Management: State of the Art, Opportunities and Challenges under Integration of Energy Internet and Industrial Internet

Industrial power has a large load base and considerable adjustment potential. Enterprises with a high degree of automation and adjustable potential can automatically adjust the production status according to the peak load, frequency of the power grid and the demand of new energy consumption, so as to realize automatic demand response. This paper analyzes the opportunities and challenges of industrial demand response under the integration of Industrial Internet and Energy Internet. At the same time, the development direction of industrial demand response under the new situation, such as comprehensive demand response, adjustable load resources and other technical and policy aspects are prospected.

Harnessing the Flexibility of District Heating System for Integrating Extensive Share of Renewable Energy Sources in Energy Systems

Lately, the European Union has reinforced the targets set to cut back carbon emissions. The energy generation sector and particularly, the district heating (DH) system, is still prevailed by combustion of fossil fuels that heavily contributes to such emissions. This paper presents a system-based approach to study the coupling between electricity and DH sector for effective mitigation of emissions. A mixed integer linear programming framework is proposed that aims to exploit the flexibility of electricity cogeneration together with partial electrification of the DH system by investing in renewable technologies. The objective is to simultaneously minimize the investment cost and emissions. Both the electricity and DH load profiles are segregated into critical and flexible types. Comprehensive demand response (DR) framework of thermostatically controlled loads and electric vehicles is considered while preserving the chronology. The framework is applied to the Finnish energy system considering the generation mix. Results prove that coordinating the electricity cogeneration with renewable generation combined with partly shifting from DH to electrified heating has a great potential in reducing the emissions. For an average weather scenario under DR, the least-cost solution guarantees an annual emission reduction of 12.04% relative to the total emissions of Finland against the total investment of €13.24Bn in wind and solar power generation.

Optimal scheduling of Park level integrated energy system considering electrothermal coupling

Considering the inherent characteristics of the park heating load, such as transmission delay, fuzzy heating comfort, etc., it can be used as a flexible load to participate in the optimal scheduling. Aiming at the minimum operation cost of the integrated energy system in the park, a collaborative optimal scheduling model of the park's integrated energy system with the participation of comprehensive demand response of electric heating load is constructed. The simulation results show that, compared with the optimization results of traditional power demand response, the application of integrated demand response of electric heating load improves the flexibility of production of cogeneration units in the park, reduces the total energy consumption cost of demand side users and the operation cost of the system on the premise of ensuring the balance of supply and demand of the system, improves the energy utilization efficiency, and realizes the environmental protection and economy of the system function.

Economic Dispatch Analysis of Comprehensive Energy System Considering Carbon Trading

In the context of energy internet and low-carbon power, integrated energy systems have become an important carrier for energy saving and emission reduction. Aiming at the schedulable value of integrated energy service providers including electricity, heat and natural gas loads in the electricity market and the natural gas market, a low-carbon dispatch model for power systems considering the response of integrated energy service providers was established. The carbon trading mechanism is introduced into the system’s scheduling model, and a ladder calculation model for calculating the carbon trading cost in the carbon emission interval is constructed; considering the carbon trading cost and the cost of outsourced energy, a system suitable for electricity, heat and gas supply is established Low-carbon economic dispatch model. By comparing and analyzing the scheduling results of the three scheduling models, the effectiveness of the proposed model in considering the low-carbon and economic aspects of the system is verified. The analysis of the example shows that considering the response of integrated energy service providers in the electricity and natural gas market can reduce the carbon emissions of the power system and the operating cost of the entire system. The carbon trading mechanism and the comprehensive demand response are managed from the power generation side and the load. Effective means.