Integrated Community Energy System Research Articles

Integrated energy system optimization and scheduling method considering the source and load coordinated scheduling of thermal-storage electric boilers and electric vehicles

The northern regions of China face the challenges of the mismatch of the power supply and demand, as well as serious wind curtailment issues, caused mainly by the limitation of the “with heat to determine electricity” mode for combined heat and power generation during the winter season. To further absorb the surplus wind power and alleviate restrictions, a comprehensive energy system optimization method for parks based on coordinated scheduling between sources and loads is proposed in this paper. First, the implementation of a heat-storage electric boiler on the source side further achieves the decoupling of heat and power. Second, an optimized scheduling method for electric vehicles combining incentive scheduling and orderly scheduling is proposed on the load side, which helps flatten the load curve. Finally, a tiered carbon trading mechanism is introduced and a community integrated energy system (CIES) optimization scheduling model is established with the aim of minimizing the total cost of the CIES, and the problem is solved using the CPLEX commercial solver. The simulation results indicate that the overall system efficiency is significantly improved through the coordinated scheduling of power sources and loads. Specifically, the integration rate of wind power increases by 3.91% when compared to the sole consideration of the integrated demand response. Furthermore, the peak shaving and off-peak filling effect is considerably enhanced compared to the utilization of only thermal-storage electric boilers. Additionally, the implementation of coordinated scheduling leads to a reduction in the total system cost by 2764.32 yuan and a decrease in total carbon emissions by 3515.4 kg. These findings provide compelling evidence that the coordinated scheduling of power sources and loads surpasses the limitations of thermal power units, strengthens the demand response capability of electric vehicles, and enhances the economic benefits of the CIES.

Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading

To alleviate the challenges posed by high energy consumption, significant carbon emissions, and conflicting interests among multiple parties in a community-level microgrid, the authors of this study propose a master–slave game-based optimal scheduling strategy for a community-integrated energy system (CIES). First, we analyze the decision variables and revenue-related objectives of each stakeholder in the CIES, and use the results to construct a framework of implementation. Second, we develop a model to incentivize peak regulation and a ladder-type carbon trading model that consider the correlation between the load owing to residential consumers, the load on the regional grid, and the sources of carbon emissions. Third, we propose a master–slave game-based mechanism of interaction and a decision-making model for each party to the game, and show that it has a Stackelberg equilibrium solution by combining genetic algorithms and quadratic programming. The results of evaluations showed that compared with an optimization strategy that considers only the master–slave game, the proposed strategy increased the consumption surplus of the user aggregator by 13.65%, the revenue of the community energy operator by 7.95%, increased the revenue of the energy storage operator, reduced CO2 emissions by 6.10%, and adequately responded to peak-cutting and valley-filling by the power grid company.

Multi-Objective Optimal Scheduling of Community Integrated Energy System Considering Comprehensive Customer Dissatisfaction Model

Based on the system with combined cooling, heating and power, this paper formulates a multi-objective balance optimization scheduling framework of community integrated energy systems, in which demand response programs are considered. To quantify the customer's dissatisfaction caused by the changes in energy use style and a decline in comfort, the electricity dissatisfaction function and heat/cold dissatisfaction function are constructed based on Kano's model. Then, a comprehensive customer dissatisfaction model is proposed based on a combination of subjective weight and objective weight method. Furthermore, a multi-objective optimization model is constructed to minimize total operating costs, comprehensive customer dissatisfaction and carbon dioxide emissions. The case study is considered in two different customer dissatisfaction functions: the linear customer dissatisfaction function and Kano's model-based customer dissatisfaction function. Finally, non-dominated sorting genetic algorithm (NSGA-II) is utilized to simultaneously optimize three goals and get the Pareto fronts. From the obtained Pareto fronts, the best scheme is obtained by using the entropy-weighting Similarity to Ideal Solution (TOPSIS) method. The results show that the proposed customer dissatisfaction model can effectively reduce the operating costs, customer dissatisfaction, and carbon emissions, under the premise of balancing the three objective functions, compared with the linear customer dissatisfaction model.

Stackelberg game-based optimal scheduling for multi-community integrated energy systems considering energy interaction and carbon trading

The multi-community integrated energy system (MCIES) has become a practical approach to improve energy efficiency and reduce carbon emissions with the promotion of urbanization. However, the current optimal scheduling of MCIES neither balances the total operation cost of the MCIES and the individual benefits of community microgrids effectively nor considers the carbon emission accounting and carbon trading of terminal integrated energy systems adequately. In this study, a bi-level optimal scheduling framework dominated by the regional dispatching center (RDC) and fully participated by the community integrated energy systems (CIESs) is developed for the scheduling of MCIES, which considers the energy interaction and carbon trading between CIESs. Firstly, a refined carbon emission accounting model considering direct and indirect carbon emissions is proposed, which can calculate the carbon emissions of each CIES in detail from the energy flow perspective based on the definition of virtual and actual carbon flow. Then, a one-master-multi-slave game model is established, where the RDC acts as the leader pursuing the minimum operation cost of the region by formulating energy interaction and carbon trading schemes between CIESs, while the CIESs are regarded as the followers that adjust the energy consumptions to minimize their operation costs. Finally, the effectiveness of the proposed optimization model is validated through the case studies, which are tested in a specific urban area. The simulation results demonstrate that the proposed optimal scheduling model can not only effectively coordinate the benefits between RDC and CIESs, but also reduce the overall operational cost and carbon emission of the MCIES.

Model-data-event based community integrated energy system low-carbon economic scheduling

With environmental pollution becoming an increasingly serious problem, it is crucial to develop renewable energy and improve energy utilization efficiency. Integrated energy system realizes complementary utilization of various energies by integrating various heterogeneous energies. However, the uncertainty of generation and load causes random fluctuation, making it difficult to accurately and quickly adapt to the dynamic environment. Therefore, this research designs a model-data-event based low-carbon economic scheduling framework for the community integrated energy system. Firstly, the low-carbon economic scheduling problem is mathematically described, then the decision-making problem is expressed as a reinforcement learning framework. An improved DDPG algorithm which can cope with the uncertainty is used to make real-time scheduling decisions. Considering the instability of reinforcement learning algorithm, this research proposes a heuristic real-time power allocation strategy, which comprehensively considers the degree of imbalance and the schedulable ability of the responders. The event-driven mechanism enables the responders to respond quickly and accurately to the energy compensation requirements. Simulation results indicate that the proposed scheduling framework can adapt to different parameters and reduce energy costs up to 6.65% and 3.85% compared to the deep Q network and soft actor critic algorithms, with the computational efficiency 11 times higher than that of model predictive control algorithm.

Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization

The transition of the energy model dominated by centralized fossil energy use and the emergence of the Energy Internet and the Integrated Community Energy System (ICES) has gained attention. ICES involved the connection of electricity, heat, gas, and other kinds of energy, and was a significant form of the targeted transformation of conventional single energy networks. Within this system, the traditional demand response (DR) was transformed into an integrated demand response (IDR) in which all energy consumers could participate. The purpose of this study is to discuss the important technologies and models along with assessment and optimization strategies for the implementation of ICES and IDR, based on an extensive literature review. The analysis results show the “IDR + ICES” ecosystem proved to hold great potential for achieving renewable energy penetration, energy efficiency, and climate change control goals, while there are still many limitations in the coordination and reliability of the model and the design of the market mechanism. To conclude, the challenges and opportunities that ICES and IDR face were summarized, and future avenues for research are outlined.

Optimization scheduling of a wind–photovoltaic–gas–electric vehicles Community-Integrated Energy System considering uncertainty and carbon emissions reduction

Coupling renewable energy power generation, electric vehicles, combined cooling heating and power system, and energy storage system is a new way for Community-Integrated Energy Systems (CIES) to shift to a low-carbon, highly efficient, environmentally friendly society, which is the key task in coordinating flexible demand response with multiple uncertainties in renewable energy. To this end, an integrated demand response program is used to explore the potential interaction ability of electricity-gas-heat-cool-Electric Vehicles flexible loads in a scheduling model based on chance-constrained programming, based on which an integrated demand respond-enabled CIES with uncertain renewable generation was built, meanwhile, a ladder-type carbon trading mechanism was designed, and then the original model was transformed into a solvable mixed-integer linear programming model using Sequence Operation Theory and linearization methods. Furthermore, the objective function was constructed by using the deviation preference optimization strategy, daily comprehensive cost and daily carbon emissions were introduced to reflect the decision-makers’ different optimization preferences by using weight coefficients as double optimization objectives. Finally, the simulation shows that CIES operation can effectively balance economy and reliability by setting an appropriate confidence level for the spinning reserve constraint, meanwhile, the comparative result shows that EV charging in the CIES has a very significant environmental benefit in carbon dioxide emissions reduction, while the number of EVs exceeds 500, EV charging in the CIES starts to show a better economic benefit.

Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and ∞-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models

The economics of integrated community energy system (ICES) dispatch schemes are influenced by the accuracy of the parameters of the different energy-conversion-equipment models. Traditional equipment efficiency correction models only take into account the historical load factors and variations in the environmental factors, ignoring the fact that the input data do not come from the actual operating data of the equipment, which affects the accuracy of the equipment models and therefore reduces the economics of ICES dispatch solutions. Therefore, this paper proposes an optimal scheduling of a community-integrated energy system based on twin data, considering a device-correction model that combines an energy hub model and a twin data correction model. Firstly, a dynamic energy hub (DEH) model with a correctable conversion efficiency is developed based on the twin data; secondly, a physical model of the system and a digital twin are established, with the prediction data as the input of the digital twin and the twin data as the output. Polynomial regression (PR) and a back propagation neural network (BPNNS) are used to process the twin data to accurately extract the equipment conversion efficiency. Considering the lack of accuracy of traditional prediction methods, a prediction model combining a long- and short-term-memory neural network and digital-twin technology is constructed for renewable energy generation and load prediction. The simulation results show that using twin data to correct the equipment efficiency reduces the average absolute error and average relative error by 4.6706 and 1.18%, respectively, when compared with the use of historical data. Compared with the actual total cost of the dispatch, the total cost of the dispatch after the equipment efficiency correction was reduced by USD 850.19.

Optimal dispatch of community integrated energy system based on Stackelberg game and integrated demand response under carbon trading mechanism

• To propose a more comprehensive, complete and detailed CIES planning model • To propose a co-optimization strategy for CTM and IDR on supply and demand sides. • To propose an SMMS game model that optimizes the three parties in the community. • To propose an energy supply location strategy for improving the supplier's profit. The integrated energy system (IES) is an effective way to meet multi-energy needs, improve energy utilization, and reduce pollution emissions. However, in the existing research, the coordinated optimization of carbon trading mechanism (CTM) and integrated demand response (IDR) has not been fully considered in the operation of IES. Therefore, a game optimization model of single-master-multiple-slaves (SMMS) considering CTM and IDR for the community integrated energy system (CIES) is proposed. First, a comprehensive CIES framework is proposed. CTM and IDR are modelled and analyzed. In addition, the community users are divided into office worker aggregators and retiree aggregators. The community integrated energy service provider (CIESP) acts as the leader, and the two user aggregators act as followers to optimize the game model of SMMS. Finally, the effectiveness of the proposed coordination optimization model is verified through multiple case studies. The results show that the profit of CIESP increased by 15.48%, and the consumer surplus of office worker aggregator and retiree aggregator increased by 9.34% and 9.82%, respectively. The operational strategies under different user participation degrees are also analyzed. The results also show that CIESP can choose a community with a higher participation degree of office workers to obtain more benefits.

Two-stage robust optimization for space heating loads of buildings in integrated community energy systems

A two-stage robust optimization (RO) method for buildings’ space heating loads (SHLs) in an integrated community energy system (ICES) is proposed. At the first stage, a bi-level optimization is deployed to formulate the hierarchical relationship between the ICES operator and consumers in buildings to enable the optimal heating pricing strategy between them. Thermal inertia of SHLs, which is modelled utilizing the Resistor-Capacitor thermal network, is used to provide heating demand response according to the optimal heating sale prices released by the ICES operator. At the second stage, the ICES operator decides the optimal energy purchase schedules from the upper energy systems after the heating sale prices are decided at the first stage. Since the day-ahead energy prices differ from the real-time ones, a RO method is adopted. The original min–max RO problem is converted into its dual problem to cooperate with the bi-level optimization. Finally, the whole optimization model is transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucker conditions, duality theory, big-M method and piecewise linearization. Numerical results show that the proposed model can balance the interests between the ICES operator and consumers. And the profit of the ICES operator is 5.10% higher using RO method, compared to the deterministic optimization under energy prices uncertainties. Compared to the benchmark solution method, the computation efficiency of the final MILP model in this paper is highly improved and the convergency of the MILP model can be guaranteed.

Demand response management of community integrated energy system: A multi-energy retail package perspective

The community integrated energy system (CIES) is becoming an effective way to improve the energy utilization efficiency and reduce the carbon emission. Demand response in a CIES may be more challenge with the participation of multi-energy consumers. In this paper, we propose a novel multi-energy retail package mechanism, which provides a new perspective for demand response management in CIES. Firstly, we describe the operation rules of the energy retail package mechanism proposed in this paper, aiming at promoting the electricity and gas loads peak shaving and the electricity load valley filling in electricity-gas CIES. Secondly, a bi-level optimization model is built to optimize a set of packages with different response requirements and discounts for different consumers. The lower model adopts Multinomial Logit theory to simulate the consumer's package selecting and energy use behavior when facing different energy retail packages while the upper model optimizes package parameters with the objective of maximizing CIES operator increased benefit. Finally, the effectiveness of the energy retail package mechanism designed in this paper for demand response management is verified through example analysis.

Adaptive forecasting of diverse electrical and heating loads in community integrated energy system based on deep transfer learning

The economic operation and scheduling of community integrated energy system (CIES) depend on accurate day-ahead multi-energy load forecasting. Considering the high randomness, obvious seasonality, and strong correlations between the multiple energy demands of CIES, this paper proposes an adaptive forecasting method for diverse loads of CIES based on deep transfer learning. First, a one-dimensional convolutional neural network (1DCNN) is formulated to extract hour-level local features, and the long short-term memory network (LSTM) is constructed to extract day-level coarse-grained features. In particular, an attention mechanism module is introduced to focus on critical load features. Second, a hard-sharing mechanism is adopted to learn the mutual coupling relationship between diverse loads, where the weather information is added to the shared layer as an auxiliary. Furthermore, considering the differences in the degree of uncertainty of multiple loads, dynamic weights are assigned to different tasks to facilitate their simultaneous optimization during training. Finally, a deep transfer learning strategy is constructed in the forecasting model to guarantee its adaptivity in various scenarios, where the maximum mean discrepancy (MMD) is used to measure the gradual deviation of the load properties and the external environment. Simulation experiments on two practical CIES cases show that compared with the four benchmark models, the electrical and heating load forecasting accuracy (measured by MAPE) increased by at least 4.99 and 18.22%, respectively.

Community Integrated Energy System Multi-Energy Transaction Decision Considering User Interaction

With the gradual liberalization of China’s energy market, the distributed characteristics of each entity in the community integrated energy system are more and more obvious, and the traditional centralized optimization is difficult to reveal the interaction between the entities. This paper aims to improve the profit of the community operator and the users’ value-added benefit of energy use, and proposes a multi-energy transaction decision of a community integrated energy system considering user interaction. First, a refined model of user interaction, including energy conversion, is established, and then the optimization model of multi-energy transaction decision between the community operator and the users is constructed based on the master–slave game. The upper layer aims to maximize the profit of the community operator according to the energy use strategies’ feedback from the users, decides the retail energy prices of the community operator to the users, and optimization variables include equipment output and energy purchased from the power grid and natural gas grid. The lower layer aims to maximize the value-added benefit of energy use for users. The users optimize their energy use strategies based on the retail energy prices published by the community operator. The model is solved by the differential evolution algorithm combined with the CPLEX solver. Finally, different scenarios are analyzed in a numerical example, and the results show that the strategy proposed in this paper to set community prices increases the community operator’s profit and profit margin by 5.9% and 7.5%, respectively, compared to using market energy prices directly. At the same time, the value-added benefit to users also increases by 15.2%. The community operator and users can achieve a win–win situation.

Surrogate model assisted multi-criteria operation evaluation of community integrated energy systems

Operational evaluation is critical for revealing the operational performance and improvement potential of community integrated energy systems (CIESs). However, complex system configuration and flexible energy conversion in a CIES render the evaluation more challenging. Both the loss in energy quality and quantity should be considered to comprehensively evaluate the energy utilization capability of a CIES. Accurately quantified evaluation requires the optimal solution as the reference, which incurs a heavy computational burden and limits its applicability under massive scenarios. This paper proposes a surrogate model assisted quantified evaluation method for CIES operation considering the variation in energy quality and to show the gap to optimal performance. First, exergy theory is adopted to analyze the matching degree between the input and output energy quality. Second, three indicators are considered as examples to reveal the operational performance in terms of system efficiency, renewable energy penetration, and operation cost. The scores of quantified evaluation are derived using the technique for order preference by similarity to an ideal solution (TOPSIS) method. Third, a CNN-based surrogate model is designed to replace the time-consuming optimization problems for the optimal reference solution, which effectively accelerates the evaluation efficiency under massive scenarios. Case studies demonstrate the effectiveness of the evaluation methods and efficiency of the proposed surrogate model.

A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model

The integrated community energy system (ICES) is an effective means to promote the synergies among multiple energy carriers. However, the off-design performance of equipment challenges the accurate and economical scheduling of the ICES. To solve this problem, a two-stage scheduling method for the ICES based on a hybrid mechanism and data-driven model is proposed in this paper. Combing the mechanism energy hub (EH) model with a data-driven efficiency correction model, a hybrid-driven dynamic energy hub (DEH) with variable equipment efficiency is built first. The EH describes the multi-energy coupling relationship; the embedded efficiency correction model adopts data-driven approaches of polynomial regression (PR) and backpropagation neural networks (BPNNs) to accurately extract nonlinear characteristics of equipment efficiency. On this basis, a two-stage scheduling model for the ICES is developed. In the day-ahead stage, the PR method is applied to calculate equipment efficiency which varies with load rate. The day-ahead scheduling model is established with the aim of minimizing the operating cost. In the intraday stage, considering the effects of load rate, temperature, and atmospheric pressure, the BPNNs method is employed to further correct equipment efficiency using the latest data. Furthermore, a rolling optimization (RO) strategy is used to address the uncertainties of equipment efficiency and load demand to improve the accuracy and economy of the scheduling scheme. Case studies demonstrate that the proposed method can improve the solution speed and accuracy of the scheduling model, and enhance the operating economy of the ICES.

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.

Optimal Dispatch of Community Integrated Energy System Considering Comprehensive User Satisfaction Method

In this article, a community integrated energy system (CIES) coupled with electricity, gas and heat is investigated, which integrates with renewable energies such as photovoltaic and wind turbine. The main goal is to seek an optimal scheduling scheme, including reducing the operating costs and fully exploiting the demand response potential. Firstly, given the structure of CIES, the system equipment and loads are modeled and analyzed. Moreover, considering the user controllable degree, a comprehensive user satisfaction method is established based on the combination of subjective weight and objective weight method. This paper concerns the user controllable degree in the process of flexible load optimization so that some amounts of loads are transferred from peak periods to off‐peak periods to flatten the load curve. Three cases studies in the article show the influence of user controllable degree and satisfaction on CIES. Finally, the system model is solved using CPLEX solver in MATLAB optimization software. The results show that the operating costs and environmental costs are reduced by 9.31% and 3.24% due to the presence of user controllable degree and satisfaction. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Stackelberg Game-based planning approach for integrated community energy system considering multiple participants

The game relationship between the Energy Server (ES) and the Energy Consumer (EC) in the Integrated Community Energy System (ICES) can change the energy supply and consumption behaviors of both participants, and affect the economy of the ICES planning scheme. This paper proposes a bi-level model to address a joint master-slave planning-operation problem of the ICES. Firstly, the revenue models of the ES and EC in the ICES is established. Secondly, a bi-level planning model based on Stackelberg Game for the ICES is proposed. In the upper level, a joint optimal planning model of energy conversion devices and energy prices is built and used by the ES (game leader) to maximize its profit. In the lower level, an optimal energy consuming strategy is proposed for the EC (game follower) to minimize its energy bill. The Karush-Kuhn-Tucker (KKT) conditions are utilized to convert the bi-level planning problem to a single-level mathematical program with equilibrium constraints (MPEC). A typical ICES is employed as a test system to illustrate the effectiveness of the proposed bi-level planning approach, and the results show that the proposed approach can reduce the investment cost of ES while ensuring the interests of EC.

Optimal Dispatching Modeling of Regional Power–Heat–Gas Interconnection Based on Multi-Type Load Adjustability

As an important research direction of the energy system, the integrated energy system of a park plays an important role in the field of energy optimization and sustainable economic operation. In this study, a low-carbon optimal operation model of the integrated energy system of an industrial park is proposed, considering a controllable flexible load response. First, the typical structure of the integrated energy system of the park and the model of each subsystem are introduced; then, under the premise that flexible electrical and thermal loads can be used for adjustment of energy utilization, a complete dispatch scheme is constructed according to the energy consumption and system operation characteristics of the integrated energy system. Finally, an optimal scheduling model for the combined supply and demand of the integrated energy system is established with the aim of minimizing the total operating cost. For experimental results, the YALMIP toolbox and the CPLEX solver are used to verify the results of the study; simulation results show that the optimal scheduling of controllable loads can effectively reduce the comprehensive operating cost of a community integrated energy system.