Integrated Community Energy System Research Articles

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

A Two‐Stage Multi‐Objective Optimal Scheduling Model for Community Integrated Energy System

Community integrated energy system coupled with renewable energy generation provides an effective solution to improve economy and reduce carbon emissions. This paper establishes a two‐stage multi‐objective optimal scheduling model for community integrated energy system. During the day‐ahead scheduling stage, a comprehensive customer dissatisfaction model based on Kano model is established, and the model takes total operating cost, comprehensive customer dissatisfaction, and carbon emissions as multi‐objectives. The Non‐dominate Sorting Genetic Algorithmic‐II (NSGA‐II) and the CRITIC‐TOPSIS evaluation model are used to develop a day‐ahead scheduling scheme. On the premise of ensuring customer dissatisfaction, the intra‐day scheduling stage evens out the uncertainty of wind power and photovoltaic power through rolling optimization and improves the reliability of system operation. Simulation results show that the proposed model reduces the total operating cost and carbon emissions by 8.1% and 12.8%, respectively, which validates the effectiveness of the proposed model. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Hybrid data-driven operation method for demand response of community integrated energy systems utilizing virtual and physical energy storage

Community integrated energy systems (CIES) have become flexible contributors to DR within distribution networks. Buildings’ thermal capacities can serve as virtual energy storage (VES) to augment CIES flexibility. However, uncertainties in environmental factors and building parameters hinder the accurate use of virtual energy storage without compromising user comfort. In addition, a single type of flexible resource is usually insufficient to meet the complex DR requirements of distribution system operators, underscoring the need for coordinating physical and virtual energy storage. This study proposes a hybrid data-driven operational approach to enhance the DR of a CIES. Real-time measurements were employed instead of exact system models and parameters to effectively dispatch VES during DR events. Additionally, rolling robust optimization was implemented to coordinate physical and virtual energy storage for DR, accommodating various uncertainties and multi-timescale characteristics within the CIES. Through its application to a case study, the proposed method demonstrated its efficacy in leveraging VES in a CIES for DR without compromising users’ thermal comfort. In a typical power reduction scenario, the DR revenue increased by 39.6%, while the overall operational cost decreased by 17.0%. In the power increase scenario, the DR revenue increased by 122.0%, and the overall operational cost decreased by 20.0%.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated.

Optimised operation of integrated community energy system considering integrated energy pricing strategy: A two-layer Stackelberg game approach

Aiming at the problems of new energy absorption and inter-subject interest conflict in the integrated community energy system, this study proposed an integrated energy pricing strategy that considered the interconversion of various energy sources and built a two-layer optimisation model of the electric-gas-heat‑hydrogen interconnection integrated community energy system. The upper-layer operator model maximises the revenue by setting energy price and selling energy to users. The lower-layer users model minimises the energy cost by actively adjusting energy usage strategy according to energy price. The two sides rationally pursue the maximisation of their respective interests, and the process of bargaining can be described by Stackelberg game. The model is also considered for combining the characteristics of elastic load and vehicle-to-grid on the demand side and operates jointly with the bus battery swapping station and two-stage power-to-gas for optimizing system operation. Through theoretical derivation, it is proved that there is a unique Stackelberg equilibrium solution for the proposed game model. Further, the two-layer optimisation model is transformed into a mixed integer quadratic programming problem by applying the Karush-Kuhn-Tucher optimal condition, linear relaxation technique, and duality theorem. Finally, the global optimal energy price is obtained by solving the converted model. The optimisation results in different scenarios show that the proposed model and method not only protect the interests of the operator and users but also improve the wind power absorption capacity of the community and reduce the load fluctuation.

Data-driven flexibility evaluation methodology for community integrated energy system in uncertain environments

Flexibility operation and evaluation have become central and critical for grid integration of high percentage of renewable energy generations (REGs). However, the interactions between multiple energy forms in community integrated energy system (CIES) and the uncertainty of REG significantly complicate the quantification of flexibility. To this end, a novel two-stage data-driven flexibility evaluation methodology is proposed for CIES. The first stage performs optimal scheduling of multiple flexible resources, where the flexible thermal comfort requirements of users in integrated response demand are considered by introducing a predicted mean vote index. The second stage refines the flexibility supply model for flexible resources, and carves out the system flexibility supply domain by enveloping the net load. To overcome the uncertainty of REG, a data-driven scenario analysis approach is developed, which fully captures the renewable output characteristics under different climate environments and avoids complicated explicit feature model construction. Furthermore, a set of evaluation metrics is proposed from the aspect of direction, amount, and frequency to quantify the flexibility. The effectiveness and accuracy of the proposed methodology are verified by applying it to a modified CIES located in North China. The sensitivity analysis of multiple time scales and flexible resources to operational flexibility and economy of CIES is presented, which provides guidelines for the efficient allocation of flexible resources and expand planning of CIES.

Co-optimization of system configurations and energy scheduling of multiple community integrated energy systems to improve photovoltaic self-consumption

Given the increasing integration of building photovoltaics and the accelerated transformation of power systems, the supply-demand imbalance in photovoltaic communities has become a pressing issue. This study presents a novel optimization model for collaborative planning and scheduling. The alternating direction multiplier method is utilized to derive an optimal economic scheduling strategy for the community cooperative alliance, while ensuring private information protection. Additionally, the genetic algorithms are employed to ascertain the optimal configuration. The optimization model prioritizes the common good over individual interests in community cooperation alliances, meaning some members may need to compromise their self-interest to attain optimized overall benefits. Therefore, a compensation mechanism based on the Nash negotiation principle is established to ensure fair distribution of benefits. The simulation results demonstrate that the primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of the photovoltaic community alliance are 42.91%, 32.50%, and 41.81%, respectively, and the self-consumption level of photovoltaic is vastly improved to 98.83% on a typical winter day. The sensitivity analysis illustrates that the photovoltaic panels in buildings contribute to a decrease in energy consumption and carbon emission by 25.57% and 26.15%, respectively. In contrast, they result in a cost increase of 11.05%.

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

Two-Stage Optimal Scheduling of Community Integrated Energy System

Two-Stage Optimal Scheduling of Community Integrated Energy System

Stackelberg game theory based model to guide users’ energy use behavior, with the consideration of flexible resources and consumer psychology, for an integrated energy system

Users' energy use behavior has great impact on the economic and efficient operation of integrated energy systems (IES). This paper proposes energy usage strategies based on integrated demand response (IDR) by developing a Stackelberg game based simulation model, in which the user-side flexible resources (FRs) and comfort cost are considered. In the paper, a framework of community integrated energy system (CIES) and an IDR model considering the consumer psychology were established first, in which the community users were viewed as a user aggregator (UA). A Stackelberg game simulation model was then developed for studying the energy interactions between community integrated energy service provider (CIESP) as the leader and UA as the follower based on the proposed IDR model. The benefit gain and energy use behavior of the UA were analyzed, by using the model developed, in six scenarios. The study found the strategy developed with the consideration of FRs and consumer psychology could make the UA's cost of purchasing energy reduced by 17.14 %. Meanwhile, the consumer surplus could increase by 11.58 %. The results also showed that by introducing user-side gas boilers and distributed energy storage more benefit gain for users and improved load regulation capabilities for CIES could be achieved.

Risk-managed operation of community integrated energy systems in day-ahead and real-time markets based on portfolio theory

Operation strategy is a critical issue for the community integrated energy system (CIES) to improve its economic performance. However, the uncertainties in energy markets may result in severe deviation and fluctuation in the operation cost. It is challenging to balance the cost and risk in the operation optimization of CIES. This paper proposes a risk-managed operation optimization method based on portfolio theory for the CIES in day-ahead and real-time electricity markets. First, both kinds of markets are modeled as risky investment markets, whose returns and risks are described by the mean value and the variance of the electricity price. Second, the power purchase of the CIES from different markets is modeled as risky investment activities, in which the Markowitz portfolio theory is employed to balance the risk and cost. Third, the optimal portfolio can be obtained by solving the operation optimization problem of the CIES considering the risk preference of the owner. The case studies show that the CIES can reduce price volatility by 10.11% with only a 3.46% increase in cost in the day-ahead market. The proposed method provides a balance between different returns and risk fluctuations according to user preferences, which verified the validity and feasibility.

Dual-Stackelberg game-based trading in community integrated energy system considering uncertain demand response and carbon trading

Given the uncertainties in integrated demand response and carbon trading mechanism, this paper utilizes a dual-Stackelberg game framework to propose trading strategies among multiple entities within such a system. The innovation of this study lies in its development of a collaborative optimization method that encompasses extensive competition between the supply and demand sides, considering the synergistic incentives of carbon trading on the source-side and uncertain responses on the load-side. Firstly, a typical system framework is constructed involving various participants, clarifying energy and carbon market mechanisms and incorporating users’ psychological characteristics in demand response uncertainty. Secondly, a dual-Stackelberg game model is established, and the existence and uniqueness of game equilibrium are demonstrated. Finally, the model's effectiveness is validated through various scenarios. The results indicate that the equilibrium benefits of each entity are 6643.81, 7747.16, 8029.01 yuan, and 9326.62 yuan. The carbon trading mechanism can effectively constrain and reduce the system's carbon emissions by 10.20 %. Furthermore, accounting for user uncertainties in integrated demand response behavior leads to an 18.54 % increase in the peak-to-valley difference in electricity demand. It is concluded that the presented methodology provides innovative insights for addressing trading challenges within hierarchical integrated energy markets.

Optimal Operation Strategy of Community Integrated Energy System Based on a Two-level Game

With the gradual transformation of the integrated energy system to intelligence and decentralization, users with distributed generation gradually replace traditional users. To realize the rationality of multi-agent power transactions in a community-integrated energy system and improve the income of each subject in the system, this paper presents the optimal operation strategy of a community-integrated energy system based on the two-level game. The first layer is a cooperative game between energy communities, which can promote the consumption of new energy in communities. The second layer is a master-slave game between operators and communities, which can promote effective interaction between the upper and lower levels. By using the Karush-Kuhn-Tucker condition, the two-layer model is transformed into a single-layer model and uses McCormick to relax the model. The example verifies that the trading strategy proposed in this paper can effectively improve the benefits of each energy community.

Reliability-constrained planning of community integrated energy systems based on fault incidence matrix

Reliability is a prerequisite for community integrated energy systems to provide consumers with uninterrupted high-quality energy supply. However, it is still challenging to consider reliability in the planning of community integrated energy systems due to the complexity of energy conversion relationships. Conventional reliability-constrained planning methods usually form a bi-level optimization problem in which the planning scheme optimization and reliability index calculation are separated into two subproblems. In this paper, a novel reliability-constrained planning method for community integrated energy systems is proposed. First, the fault incidence matrices are employed to construct the analytic reliability evaluation model of community integrated energy system. Then, the reliability evaluation model is linearized and explicitly integrated into the planning model, forming a mixed-integer linear programming model. This model can be efficiently solved by mathematical optimization algorithms while effectively balancing the operation cost and reliability at the planning stage. Case studies are conducted based on a practical community integrated energy system in North China to verify the effectiveness of the proposed method.

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.