Multi-energy Coupling Research Articles

Design of Energy Management Strategy for Integrated Energy System Including Multi-Component Electric–Thermal–Hydrogen Energy Storage

To address the challenges of multi-energy coupling decision-making caused by the complex interactions and significant conflicts of interest among multiple entities in integrated energy systems, an energy management strategy for integrated energy systems with electricity, heat, and hydrogen multi-energy storage is proposed. First, based on the coupling relationship of electricity, heat, and hydrogen multi-energy flows, the architecture of the integrated energy system is designed, and the mathematical model of the main components of the system is established. Second, evaluation indexes in three dimensions, including energy storage device life, load satisfaction rate, and new energy utilization rate, are designed to fully characterize the economy, stability, and environmental protection of the system during operation. Then, an improved radar chart model considering multi-evaluation index comprehensive optimization is established, and an adaptability function is constructed based on the sector area and perimeter. Combined with the operation requirements of the electric–thermal–hydrogen integrated energy system, constraint conditions are determined. Finally, the effectiveness and adaptability of the strategy are verified by examples. The proposed strategy can obtain the optimal decision instructions under different operation objectives by changing the weight of evaluation indexes, while avoiding the huge decision space and secondary optimization problems caused by multiple non-inferior solutions in conventional optimization, and has multi-scenario adaptability.

Aggregation Modeling for Integrated Energy Systems Based on Chance-Constrained Optimization

Integrated energy systems (IESs) strengthen electricity–gas–heat multi-energy coupling and reduce wind and light abandonment. For grids with superior distribution, IESs are similar to virtual energy storage systems and are able to realize efficient interaction with the grid by synergizing the operating status of the internal equipment and improving the security, economy, and flexibility of the grid’s operation. However, the internal equipment coupling of an IES is complex, and determining how to evaluate its adjustable capacity range (that is, the upper and lower boundaries of its external energy demand) considering the uncertainty and volatility of wind power and photovoltaic output is a problem to be solved. To solve this problem, this paper presents a chance-constrained evaluation method for the adjustable capacity of IESs. Firstly, mathematical models and operational constraints of each device within the IES are established. Secondly, based on the mathematical model of chance-constrained planning, an adjustable capacity range assessment model considering the uncertainty of wind and photovoltaic output is established. Finally, the MATLAB/Yalmip/Gurobi solver is used for the optimization solution, and the adjustable capacity range interval of the constructed IES model is solved using an arithmetic example to analyze and verify the correctness and validity of the method and to study the influencing factors of its adjustable range.

Multi-Energy Coupling Load Forecasting in Integrated Energy System with Improved Variational Mode Decomposition-Temporal Convolutional Network-Bidirectional Long Short-Term Memory Model

Accurate load forecasting is crucial to the stable operation of integrated energy systems (IES), which plays a significant role in advancing sustainable development. Addressing the challenge of insufficient prediction accuracy caused by the inherent uncertainty and volatility of load data, this study proposes a multi-energy load forecasting method for IES using an improved VMD-TCN-BiLSTM model. The proposed model consists of optimizing the Variational Mode Decomposition (VMD) parameters through a mathematical model based on minimizing the average permutation entropy (PE). Moreover, load sequences are decomposed into different Intrinsic Mode Functions (IMFs) using VMD, with the optimal number of models determined by the average PE to reduce the non-stationarity of the original sequences. Considering the coupling relationship among electrical, thermal, and cooling loads, the input features of the forecasting model are constructed by combining the IMF set of multi-energy loads with meteorological data and related load information. As a result, a hybrid neural network structure, integrating a Temporal Convolutional Network (TCN) with a Bidirectional Long Short-Term Memory (BiLSTM) network for load prediction is developed. The Sand Cat Swarm Optimization (SCSO) algorithm is employed to obtain the optimal hyper-parameters of the TCN-BiLSTM model. A case analysis is performed using the Arizona State University Tempe campus dataset. The findings demonstrate that the proposed method can outperform six other existing models in terms of Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2), verifying its effectiveness and superiority in load forecasting.

Static voltage stability analysis of integrated smart energy systems

The analysis of multi-energy flows forms the cornerstone for the study of state estimation, safety assessment, and optimization in integrated smart energy systems (ISES). The interactions between various energy flows and the inherent variability of renewable energy sources often lead to significant challenges to the static stability of ISES. This paper investigates the static voltage stability of ISES under multi-energy coupling conditions through multi-energy flow analysis in electric-gas-thermal energy systems. First, a steady-state model of the ISES is constructed by representing the interconnected energy subsystems as equivalent sources and loads in the power grid. Subsequently, through the coupling elements of ISES, the power flows of the natural gas system (NGS) and the district heating system (DHS) are converted into active power in the electrical power system (EPS), resulting in an equivalent power flow equation for the ISES. Then, using Brouwer’s fixed-point theorem, the analytical sufficient conditions for solving the equivalent power flow equation are derived. Finally, a simulation model based on MATLAB/Simulink is established. The steady-state criterion for ISES is obtained in this paper, and the correctness and effectiveness of the proposed conclusions are verified by the simulation results.

Highly stable and self-powered ultraviolet photodetector based on Dion-Jacobson phase lead-free double perovskite

Metal halide perovskite materials present a wide array of opportunities for the development of high-performance optoelectronic devices, owing to their outstanding photoelectric properties. Compared with the lead-containing perovskites, stable and non-toxic organic-inorganic hybrid perovskites exhibit enhanced potential for future commercial applications. In this work, we developed a self-powered UV photodetector based on 2D Dion−Jacobson (DJ) phase lead-free double perovskite HAAg0.5Bi0.5Br4 (HA2+ = histammonium) perovskite with an on-off ratio up to 2.66 × 106, excellent specific detectivity of 6 × 1012 Jones, and response speed of 1.32 ms/118.4 μs. The light intensity related pyro-phototronic effect on 2D perovskite is thoroughly investigated. Temperature and light detection are realized through multi-energy coupling, which greatly improves the detection performance. Coupled pyro-phototronic current is calculated nearly 75 folds higher than that from the photoelectronic merely. As-prepared photodetector film shows excellent stability in normal environment, maintaining a value of 90 % to the initial performance after 500 cycles and 90 days. This work provides a new self-powered, stable and non-toxic candidate for the future UV photodetectors.

Bilevel low-carbon coordinated operation of integrated energy systems considering dynamic tiered carbon pricing methodology

The coordinated operation and management of energy and carbon emissions in an integrated energy system (IES) can effectively promote overall energy efficiency and reduce carbon emissions. However, allocating carbon emission responsibilities between transmission-level integrated energy system (TIES) and regional integrated energy system (RIES) is difficult. On the basis of carbon emission flow theory, a model for allocating carbon emission responsibility for IES is developed in this paper. Moreover, the Shapley value method is adopted to obtain the carbon emissions responsibility intervals at the RIES. A dynamic tiered carbon pricing methodology is proposed for the RIES on the basis of equitable carbon emission responsibility intervals. In addition, a bilevel coordinated operation model that imposes carbon price to more fully excavate the low-carbon benefits obtained from operating an IES is proposed. The upper-level model, namely, the TIES, explores the optimal low-carbon schedule for energy networks by imposing fixed carbon price on the energy production costs. The lower-level model, namely, the RIES, investigates the optimal low-carbon energy supply scheme of multienergy coupling equipment and imposes dynamic tiered carbon pricing to adjust the amount of electricity and natural gas consumed. After the whole bilevel model is solved iteratively, equilibrium is reached. Case studies verify the potential and efficacy of the proposed bilevel model, demonstrating superior effectiveness in reducing carbon emissions compared with existing methods.

Reinforcement learning model for incentive-based integrated demand response considering demand-side coupling

Incentive-based integrated demand response has great potential in addressing supply-demand imbalance in integrated energy system. This study first proposes an incentive-based integrated demand response model based on multi-agent reinforcement learning, to deal with the multi energy coupling on the demand-side and enhance the applicability of the incentive-based demand response program in urban integrated energy system. Then, a self-adaptive method is proposed based on consumer energy consumption behavior, which utilizes nonlinear incentive strategy to encourage consumers to provide more demand response resources. Finally, the experimental results show that considering demand-side coupling can enhance the overall benefits of incentive-based integrated demand response, achieving a win-win situation between integrated energy service provider and consumers. After implementing incentive strategy that considers demand-side coupling, profit of integrated energy service provider increased by 16.27% and the discomfort costs borne by integrated energy consumers reduced by 11.5%. This study offers a promising approach to managing the complex and variable energy consumption behaviors in urban integrated energy system, contributing to more reliable and efficient operation of urban integrated energy system.

A game model based optimisation approach for generalised shared energy storage and integrated energy system trading

In the context of integrated energy systems, the synergy between generalised energy storage systems and integrated energy systems has significant benefits in dealing with multi-energy coupling and improving the flexibility of energy market transactions, and the characteristics of the multi-principal game in the integrated energy market are becoming more and more obvious, but it is difficult to improve the flexibility of the transactions between “source-load-storage” in a one-way master–slave game structure, and the problem of how to establish an optimisation strategy for the coordination of integrated energy systems and energy storage systems is an urgent issue. How to establish a coordinated optimisation strategy of integrated energy system and energy storage system is an urgent problem. Therefore, this paper proposes a generalised shared energy storage and integrated energy system transaction optimisation method based on a two-stage game model, which improves the flexibility of the system transaction by constructing a two-stage game energy transaction model in which the subject acts as a leader and a gamer. Compared with the current one-way game model that does not consider the game on the energy storage side, the coordinated optimisation method proposed in this paper enables the energy storage side to participate more actively in the scheduling, which improves its revenue by 20.6%, the revenue on the energy-using side by 6.3%, and the overall revenue of the system by 5.4%, and at the same time, the load demand response regulation effect is more obvious, so the energy scheduling strategy proposed in this paper is able to weigh the interests of each subject and increase the overall economic benefits of the system.

Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis

Considering the seasonal and cyclical fluctuation of loads and the complexity of multi-energy coupling, this paper proposes a novel load forecasting model based on sequence decomposition fusion and factors correlation analysis. Firstly, the variational mode decomposition (VMD) is used to decompose the highly complex load sequences and the novel influencing factors correlation analysis (ICA) is proposed to select strong factors and remove weak feature variables to construct the input and output set. Secondly, this paper proposes the combined forecasting framework MTL-CNN-BiGRU-Attention to simultaneously forecast the cooling, heat, and electricity loads, along with BiGRU used as the hard sharing layer to deeply explore the coupling information between different types of loads. Meanwhile, the gray wolf algorithm (GWO) is improved to accurately and quickly search for the optimal hyperparameters of the model. Finally, the dataset of a comprehensive energy station in Shanghai is used to test our model, and the results show that the MAPE of the cooling and electricity loads forecasting achieve 5.501% and 5.821% in summer and 5.921%, 7.899% and 7.541% for the cooling, heat and electricity loads in transition season and winter, which confirms the effectiveness and superiority of our model.

Multi-objective robust dynamic pricing and operation strategy optimization for integrated energy system based on stackelberg game

The integrated energy system (IES) with hydrogen storage has become one of the most important developments in multi-energy coupling field, where the severe conflict of interests between different entities leads to great challenges to the economy and low-carbon operation. A multi-objective robust dynamic pricing and operation strategy optimization method based on the Stackelberg game is proposed for the hydrogen-containing energy storage (HES) IES. Firstly, the HES-IES trading framework is established based on the introduction of an integrated energy operator (IEO) and a load aggregator (LA). Secondly, a multi-objective robust Stackelberg game model is developed with the IEO as the leader and the LA as the follower, considering the minimization of operating costs and carbon emissions of the IEO and the minimization of integrated energy costs of the LA as the objectives. Finally, the compromise planning and the max-min fuzzy are addressed to solve the multi-objective model, which adopts the adaptive differential evolution (ADE) algorithm. In addition, the robust optimization (RO) with adjustable coefficients is employed to tackle uncertainties of source and load. The results show that this method can effectively balance the operating costs and carbon emissions of the system, improve the benefit of the IEO, reduce the costs of the LA, and avoid the uncertainty risk. Compared with traditional algorithms, the ADE algorithm has significant advantages in the number of iterations and solving time. In summary, the multi-objective robust dynamic pricing and operation strategy optimization proposed in this paper could effectively achieve the benefit balance between the IEO and the LA, also further improve the economy and robustness of the system and reduce carbon emissions.

An Optimized Operation Method for Integrated Energy System Based on Variable Operating Condition Characteristics

Abstract An integrated energy system (IES), functioning as a multi-input, multi-output apparatus, incorporates diverse power conversion and storage device. Typically, the energy hub visually demonstrates its multi-energy coupling. However, traditional energy hub models tend to ignore the variable operating condition characteristics of conversion device, that is, the actual operation efficiency fluctuates with the load rate. Consequently, it results in constant coupling coefficients in the energy hub coupling matrix, causing discrepancies between the IES operating state and actual conditions, affecting the operation scheme’s economy. Therefore, this study focuses on IES optimized operation method, which is based on variable operating condition characteristics, incorporating conversion device operating efficiency and load rate relationships into EH models. Development of a dynamic energy hub (DEH) model that takes into account the variable operating condition characteristics of the conversion device, operational efficiency of the device is corrected in real time, and an optimal scheduling model is built with the objective of optimal IES economy, designing different scenarios for simulation and verification with an IES as an example. The study demonstrates that incorporating device operating condition dynamics into scheduling scheme effectively minimizes system expenses and enhances the system’s integrity and feasibility.

Practical application mode and future development of multi-energy coupling system

Abstract Compared with the single energy structure in the past, the energy coupling system can realize the coordinated planning and mutual economic regulation among various energy systems, which plays an important role in improving the efficiency of energy utilization. At present there are many related studies but there is a lack of systematic summary. Combining the domestic actual situation, the article mainly lists several multi-energy coupling systems including EWN, IES, BPG, etc., to analyze the energy sources, structural composition and how these systems play a good role in improving energy efficiency, it also summarizes the present situation of comprehensive energy coupling system development research and the shortcomings of current research and prospects for future development are put forward.

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations.

Research on low-carbon optimization methods for regional energy interconnection systems considering multi-energy coupling characteristics

Abstract With the increasing application of multi-energy cogeneration, distributed renewable power generation, and multi-energy storage in various multi-energy systems, their efficient collaborative optimization has become the key to further improving emissions. This article utilizes energy hubs to integrate energy equipment with multi-energy coupling and multi-source supply characteristics and constructs a regional interconnected energy system that considers multi-energy coupling characteristics. A multi-objective optimization model for the above-mentioned energy system is established, including economic, low-carbon, and safe aspects such as operating costs, process emissions, and operational safety, and the raccoon optimization algorithm is applied to solve the model. The experimental results show that compared with classical scheduling algorithms, the proposed method can effectively reduce carbon emissions at a certain operating cost.

Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response

Hybrid electricity-heat-hydrogen energy system with demand response (DR) is promising in enhancing flexibility and energy efficiency. However, the multi-energy coupling and source-load uncertainties makes it challenging to efficiently schedule energy flows of electricity generation, storage and DR. To this end, this paper proposes a continues deep reinforcement learning algorithm, specifically the deep deterministic policy gradient (DDPG), for the energy management optimization. Different Markov decision processes are firstly employed to analyze and compare two kinds of incentive-based electro-thermal DR contracts, i.e., load curtailment and load shifting. Simulation results exemplify the superiority of the proposed DDPG-based scheduling incorporating electro-thermal DR in terms of economy and sustainability, leading to a 16.02 % reduction in scheduling costs for contract load curtailment and an 8.52 % reduction for contract load shifting when compared to that without DR consideration. Furthermore, the robustness of DDPG-based scheduling is verified under 60 random source-load scenarios compared with different algorithms. Compared to results obtained by DDPG, DDPG-LC and DDPG-LS reduce the mean cost by 22.15 % and 12.84 %, while their error from the theoretical optimum is only around 5 %. The results demonstrate that the approximate optimality and rapid decision-making illustrate DDPG's efficient real-time scheduling capability, thereby enhancing the system's adaptability to uncertain environments.

Unified Dynamic Simulation System for Multi-Energy Flows of Electricity, Heat, and Gas in Integrated Energy Systems

Integrated energy systems (IES) coordinate heterogeneous energy flows of electricity, heat, and gas to meet diverse load demands and enhance energy efficiency, is a new generation of energy systems that promote sustainable energy development. Simulation systems are essential tools for the analysis, control, and optimization of IES. However, due to the strong coupling of heterogeneous energy flows in IES, existing simulation software often lacks integration, typically focusing on electricity or relying on separate simulations of different energy flows. To address these challenges, this paper proposes a unified simulation technology for electricity, heat, and gas. First, the thermodynamic dynamic simulation capabilities of MATLAB/Simulink are extended using the Thermolib toolbox to create a unified modeling and simulation environment for coupled multi-energy flows. On this foundation, the energy conversion relationships and operating mechanisms of key multi-energy coupling equipment are studied, and dynamic Simulink models of the critical energy devices within the system are developed. Finally, a comprehensive dynamic simulation model for the multi-energy flows of the IES is constructed by connecting energy device models through an energy flow bus. The simulation results demonstrate that this system can effectively simulate the characteristics of electricity, heat, and gas flows across multiple time scales and the complex operating conditions of various energy devices within the MATLAB environment. This approach simplifies the multi-energy flow simulation structure and improves data transmission efficiency for multi-energy flows.

Energy big data abnormal cluster detection method based on redundant convolution codec

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value.

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

With the rapid development of global trade, a large number of goods and resources are imported and exported via seaports. Multiple thermal loads and renewable energy merge into seaports, making the energy supply and demand structure increasingly complex. The traditional seaport becomes an integrated seaport energy system (ISES). Due to the complicated energy interaction of cooling, heating, electricity, and gas subsystems, the ISES urgently require reliable and secure operation. Hence, this paper proposes a new reliability assessment method for the ISES that considers thermal inertia. Firstly, the operational structure of the ISES is established considering multi-energy coupling relationships. Then, a two-stage optimal load curtailment model is constructed with multiple thermal inertias. In addition, the reliability assessment method for the ISES is proposed based on the sequential Monte Carlo simulation method. Simulations are presented to verify the effectiveness of the proposed method.

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

With the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling energy storage systems (HECESSs) is one of the important technological pathways for energy supply and deep decarbonization. In a HECESS, hydrogen storage can maintain the energy balance between supply and demand and increase the utilization efficiency of energy. However, its scenario models in power system establishment and the corresponding solution methods still need to be studied in depth. For accelerating the construction of HECESSs, firstly, this paper describes the current applications of hydrogen storage technologies from three aspects: hydrogen production, hydrogen power generation, and hydrogen storage. Secondly, based on the complementary synergistic mechanism of hydrogen energy and electric energy, the structure of the HECESS and its operation mode are described. To study the engineering applications of HECESSs more deeply, the recent progress of HECESS application at the source, grid, and load sides is reviewed. For the application of the models of hydrogen storage at the source/grid/load side, the selection of the solution method will affect the optimal solution of the model and solution efficiency. As solving complex multi-energy coupling models using traditional optimization methods is difficult, the paper therefore explored the advantages of deep reinforcement learning (DRL) algorithms and their applications in HECESSs. Finally, the technical application in the construction of new power systems supported by HECESSs is prospected. The study aims to provide a reference for the research on hydrogen storage in power systems.