Optimization Of Integrated Energy System Research Articles

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

AbstractIn the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios.

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Coordinated scheduling of integrated electricity, heat, and hydrogen systems considering energy storage in heat and hydrogen pipelines

The increasing interdependency of electricity, heat, and hydrogen sectors in recent years provides a growing incentive to make use of the possible synergies obtained when coordinating these energy sectors from the perspective of an integrated energy system (IES). However, coordinated scheduling frameworks remain poorly developed for integrated electricity, heat, and hydrogen systems, and the energy storage potentials in heat and hydrogen pipelines have not been quantitatively analyzed. The present work addresses these issues by separately analyzing the energy storage value provided by district heat systems (DHSs) and hydrogen transmission systems (HTSs). These storage potentials are applied to develop an operation model that considers the delay characteristics of heat pipelines and hydrogen pipeline storage. Then, we develop a coordinated scheduling model for a power–heat–hydrogen IES. The analysis is facilitated by quantifying the value of energy storage in heat pipelines and hydrogen pipelines in terms of operation costs. Finally, a case study is conducted to verify the necessity of accounting for the dynamic energy storage potentials of the DHS and HTS during IES optimization. Numerical results verify the economic value of comprehensive consideration of heat and hydrogen pipeline storage, which results in higher operational flexibility of IES with lower operating cost and reduced wind power curtailment.

Improved electrical coupling integrated energy system based on particle swarm optimization

The rational utilization of energy is an important issue for sustainable development. Electrically coupled integrated energy systems can enhance energy utilization efficiency and reduce energy costs. However, traditional integrated energy system optimization has problems with local optima and slow convergence speed, which cannot fully utilize energy resources. Therefore, this study proposes an improved electrical coupling integrated energy system on the ground of particle swarm optimization algorithm. In response to the problems of local optima and slow convergence speed in traditional optimization algorithms, particle swarm optimization algorithm is introduced for system optimization. By combining PSO with simulated annealing algorithm, the possibility of PSO in global optimization is reduced. The local search ability of PSO and the global search ability of simulated annealing algorithm are used to find the optimal solution. The particle swarm optimization algorithm is used for preliminary search. When the particle falls into the local optimal, the simulated annealing algorithm is introduced for global search, and the particle is guided to jump out of the local optimal and continue searching. The experiment demonstrates that the improved algorithm has certain advantages in solving test functions. The variance, mean, and optimal values are 0.00125, 0.13874, and 0.105531, respectively, which are all better than the particle swarm optimization algorithm. The simulated annealing algorithm improved the particle swarm optimization algorithm with a high accuracy index, which eventually stabilized above 0.9, and the recall index also remained above 0.8. After 100 iterations, it had already fallen into a local optimal solution. By applying the improved hybrid optimization algorithm to the electrically-coupled integrated energy system, the distribution of various energy sources can be managed and optimized more effectively, and the overall performance of the system can be improved. Especially when dealing with complex energy scheduling and distribution problems, the algorithm can provide more stable, efficient and reliable solutions. This study can achieve efficient operation and optimized scheduling of integrated energy systems, reduce energy consumption and environmental pollution, and reduce energy costs. And it can improve the reliability and stability of energy supply, which has important application value and research significance.

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

The available flexibility capacity of the integrated energy system can be used as one of the indicators of proportions of system wind power installations, which, in turn, affects the maximum installed capacity of the system wind power, and this paper proposes a method for assessing the available flexibility of the integrated energy system at multiple timescales considering different proportions of system wind power installations. First, the framework of the integrated energy system is constructed, and based on the coupling relationship between the electrical and thermal systems, the mathematical models of the P2G, combined heat and power (CHP), energy storage equipment, and wind power generation equipment within the integrated energy system are established, and the Monte Carlo method is used to predict the wind power output in a typical scenario. Second, an integrated energy system optimization model is constructed to obtain the optimal dispatch operation of the system; the empirical mode decomposition (EMD) algorithm is used to decompose the flexibility demand curve of the system in multiple timescales. The flexibility supply capacity model of different types of flexibility resources in the system at different timescales is established, and through the comparative analysis of flexibility supply and demand at the same timescale, the upward and downward flexibility shortage probability and shortage expectation indexes at each timescale can be intuitively calculated and then weighted to constitute a comprehensive index of system flexibility assessment. Finally, the available flexibility analysis of the integrated energy system under different installed wind power capacities shows that the proposed methodology can more comprehensively assess the available flexibility capacity of the integrated energy system under different timescales, and the maximum installed wind power capacity that the system can withstand can be obtained while guaranteeing sufficient available flexibility capacity.

Whether the off-design characteristics of equipment should be considered in integrated energy system optimization design models – A case study

The operational performance of integrated energy systems (IESs) varies with their configuration. The equipment model directly influences the capacity configuration of IESs. The conventional static model assumes that equipment efficiency is constant, ignoring its off-design characteristics. For this reason, this paper establishes a dynamic model considering the off-design characteristics of the equipment. Based on the dynamic and static models, bi-level optimization models with the minimum total cost as the optimization objective are established to optimize the configuration of IESs, and the optimization results are System 1 and System 2, respectively. Then, the operational performance of the two systems under the dynamic optimal scheduling model is compared. The results demonstrate that the capacity of the absorption chiller (ABC), combined heating and power (CHP) unit in System 1 are larger than in System 2 owing to the difference in equipment models, whereas the capacity of ground source heat pumps (GSHPs) shows the opposite trend. The larger capacity of the GSHP increases the grid power consumption of System 2 but also reduces its operating efficiency. The greater operation energy consumption and grid power consumption make the operating cost, carbon tax, and total cost of System 2 under the design condition higher than that of System 1, with an increased rate of 1.38%, 3.47%, and 1.14%, respectively. As with the design condition, the carbon tax, operating cost, and total cost of System 2 are higher than those of System 1 under both summer and winter operating conditions, particularly under summer operating conditions, where the carbon tax, operating cost, and total cost increase by 2.27%, 1.24%, and 0.88% respectively. Even if the energy price changes, the carbon tax, operating cost and total cost of System 2 in a typical year are still greater than those of System 1. Therefore, it is necessary to consider the off-design characteristics of equipment in the bi-level optimization design model.

Optimized Dispatch of Regional Integrated Energy System Considering Wind Power Consumption in Low-Temperature Environment

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of “heat for electricity” and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system’s consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments.

Scheduling optimization of IES based on improved particle swarm algorithm

The integrated energy system (IES) has been a qualified candidate for solving the problems of energy utilization and environmental pollution. In this study, an improved IES scheduling optimization model based on an improved particle swarm algorithm is established, and the daily operating cost is taken as the objective function. Constraints on the energy supply and consumption sides are fully considered. The results show that with the energy storage conversion scheduling strategy participating in the optimization, the daily cost is significantly decreased by 10.5%. Compared with the genetic algorithm with elite strategy, the particle swarm algorithm is more suitable for the IES, with the lowest daily cost of $ 6, 193. Besides, the improved annealing particle swarm algorithm has better adaptability when facing high-dimensional multimodal function problems, and performs better on global search and local optimization.

The Optimal Control Algorithm of the Integrated Energy System Based on Demand Response

As an important embodiment of energy diversification demand, comprehensive energy has been widely used. Aiming at the problem of system optimization for the dynamic characteristics of multi-energy networks in the integrated energy system, this paper established the integrated energy system optimization control model. It optimized the next-day system load curve based on the integrated demand response and verified the effectiveness of the algorithm in this paper. The results show that the algorithm model in this paper can improve the flexibility and economy of the system operation. It can ensure the safety of the integrated energy system operation. It can also effectively improve the economy of the system and reduce the system operation cost and promote the consumption of clean energy, which shows the effectiveness of the optimization control algorithm in this paper.

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.

Review of integrated energy system optimization considering new energy uncertainty

As one of the ways of low-carbon development, integrated energy system enables users to have stable and reliable energy to use through efficient and safe use of energy. However, it should be pointed out that improving IES is a very difficult task, because of the uncertainties in every link from production to use of energy. Firstly, introducing IES and describes the impact of uncertainty on system optimization in the energy industry. Secondly, random optimization, robust optimization and interval optimization are analyzed to identify the advantages and disadvantages of each method. Finally, the future research direction of IES optimization operation was prospected.

BAS-PSO Algorithm for Integrated Energy System Optimization of Multiple Energy Supply Facilities

Regional low carbon will play an important role in the path of achieving the “double carbon” goal, and there are still many key technologies to be broken in its planning research. In this paper, an optimal scheduling model of integrated energy system with multiple energy supply devices is established by using Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) algorithm. First, in the scheduling model, a carbon trading mechanism is introduced and a stepped carbon trading cost model is constructed to constrain the carbon emissions of the plant. Then, using drosophila algorithm, the premise of whether wind power generation and photovoltaic power generation need to be built is determined by judging the economics of wind power generation and photovoltaic power generation in the construction area, and then the target power consumption curve and new energy power supply output curve are fitted. Then use the life cycle analysis method to analyze the carbon emissions generated by electric energy storage equipment, consider the carbon trading mechanism in the system economic operation model, and solve the model by BAS-PSO algorithm to overcome the problems of local optimum and slow convergence speed. Finally, a typical park integrated energy system is simulated to analyze the economic operation conditions and energy efficiency level of the system before and after participation in demand response. The innovation of this paper lies in considering the carbon trading mechanism and solving with the optimized BAS-PSO algorithm. Considering carbon trading can effectively improve the system wind power consumption capacity, and the optimized BAS-PSO algorithm improves the defects of traditional PSO. The simulation analysis results show that the established optimal scheduling model with multiple energy supply devices can realize the optimal operation of the integrated energy system of the park in the demand response environment; the improved BAS-PSO finds the lowest energy consumption within 100 iterations in 21 out of 50 iterations; the improved BAS-PSO algorithm reduces the energy consumption by 27.1 kW on average.

A multi-timescale optimal operation strategy for an integrated energy system considering integrated demand response and equipment response time

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively.

Research status and prospect of key problems of integrated energy system optimization model

In the traditional energy system, the operation structure of various energy sources is single, the coupling degree is not high, the efficiency of energy utilization is low, and the waste of clean energy appears, so the concept of comprehensive energy system arises at the historic moment.This paper firstly introduces the composition of the integrated energy system, summarizes the research status of the network power flow model of electric power, thermal power and natural gas, and summarizes the optimization operation method, leading to the integrated energy system source-source optimization model.The objective function, constraint condition, solution algorithm and operation cost of the optimization system are analyzed, and the key scientific problems and future research direction of the optimization management are put forward

Optimal Design of Biomass-centered Integrated Energy System under Uncertainty

To achieve the goal of carbon peak and carbon neutrality, China is actively promoting the production and consumption of clean and sustainable energy such as biomass. The optimization of integrated energy system (IES) configuration including biomass has gradually become a research hotspot. In this paper, aiming at the configuration optimization problem of biomass-centered IES under uncertainty, considering the uncertainty of user load and energy price, the maximum annual profit is selected as the objective function, and the configuration planning model of IES under uncertainty is established. Then, the artificial bee colony (ABC) algorithm is used to solve the optimization model, and the influence of uncertainty factors on the configuration optimization of IES is explored. The results of the example show that with the two-stage stochastic programming model used to deal with the uncertainty factors in the IES, the equipment configuration capacity and operating parameters in the system are changed compared with the results of the deterministic conditions. It can overcome the problem of over-optimism in the deterministic condition and reduce the system operation risk due to the random variation of uncertainty factors.

Study on the Optimal Configuration of GSHP in Regional Integrated Energy Systems Considering the Uncertainty of New Energy

The study of regionally integrated energy system optimization problems is of great significance and usefulness for the development of the energy Internet. Regional integrated energy system planning and optimization modeling is the basis for rational system configuration and economical operation. It is one of the key issues in the development of integrated regional energy systems. The optimization of a regionally integrated energy system is studied in this paper. The uncertainty of wind power generation and photovoltaic power generation and the characteristic of CHP is considered. Were established. On this basis, Considering the uncertainty of new energy, a GSHP optimal allocation model based on two-layer optimization is established. A two-level optimization model is introduced. A genetic algorithm is applied to the upper layer, and a genetic algorithm is applied to the lower layer to analyze the nonlinear programming problem. Finally, different system configuration schemes are proposed and computationally solved, combined with examples for case studies to verify the feasibility of the established regional integrated energy system optimization model.

Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system

With the increasing uncertainty of energy supply side output, fully encouraging users to participate in demand response through different types of demand response incentive mechanisms has become one of the effective ways to deal with the uncertainty of integrated energy system operation and improve the overall energy efficiency. However, in existing studies, the coordination of uncertainty handling, optimization of demand response incentive strategies, and demand response measures at different time scales have not been adequately considered in the operation of integrated energy systems. Based on these considerations, this paper proposes a multi time-scale game optimization scheduling model for Park-level Integrated Energy System considering multiple types of demand response models. In the day-ahead stage, a Park-level Integrated Energy System optimization game scheduling model based on the demand response comprehensive incentive mechanism is established, and the uncertainty of the predicted value of distributed renewable energy and multi-type energy load was characterized based on the fuzzy chance-constrained programming method. In the intraday and real-time stages, a rolling optimization scheduling model is established with the minimum cost of Park-level Integrated Energy System operator scheduling. For the proposed model, an improved particle swarm optimization algorithm and an iterative solution strategy of CPLEX solver are introduced. Finally, the simulation results of an actual case show that the proposed model can effectively improve the Park-level Integrated Energy System operator and user economy while ensuring reliability.

Optimal allocation method of multi-energy system based on hybrid optimization algorithm

With the rapid development of industry, the research of energy storage technology and renewable energy continues to be hot, and the energy industry opens the era of diversification. Multi-energy complementary has become a new trend in sustainable energy development, leading the energy industry to a new energy system of deep integration and integration of multiple energy sources. This paper proposes a hybrid optimization algorithm that combines particle swarm algorithms and Hooke–Jeeves​ (HJ) with a comprehensive evaluation index as the optimization objective, aiming to improve the speed of solving the capacity optimization of integrated energy systems. The multi-energy system configuration optimization platform that covers the index system, optimization model, and system analysis module was established to systematically solve the integrated energy system optimization configuration problem, moreover provide an important reference for integrated energy system design and implementation. Besides, the influence of optimization algorithms on the configuration results was analyzed. Taking the combination of soil source heat pump system and combined cooling, heating and power system as an example, this study quantifies and compares the optimization results and solution speeds of the hybrid algorithm and the traditional single optimization calculation. It is shown that the hybrid optimization algorithm reduces the amount of iteration steps by approximately 31% compared with the particle swarm algorithm and by approximately 48% compared with the HJ algorithm. This significantly improves the speed of the optimization computation while ensuring the accuracy of the computation results.

Optimal scheduling of integrated energy systems including hydrogen electrolyzers, HFCVs, and electric vehicles

AbstractHydrogen fuel cell vehicles (HFCVs) are promising environmentally friendly technologies that are increasingly supported by governments around the world. In the integrated energy system (IES) of this paper, by adding the method of electrolysis to hydrogen production, the electrical energy is converted into chemical energy, which cannot only absorb the new energy with fluctuating output but also meet the use of more and more HFCVs. Aiming at the newly introduced hydrogen energy conversion and consumption pathways, this paper uses a statistical analogy to model HFVCs, taking into account the energy conversion efficiency and the needs of vehicle owners. Using the GUROBI solver in the YALMIP toolbox, the IES optimization problem is formed into a MILP problem and solved, and the corresponding co‐optimization scheduling method is given. Based on meeting the needs of HFCVs and electric vehicle (EV) owners, the system can schedule the number and timing of charging and discharging of EVs and the power of hydrogen production from electrolyzers on the time scale, and finally smooth the total power curve. Simulation results show that the proposed collaborative optimal energy scheduling method can meet the current demand for new energy in the system and improve the economy of the IES.

An integrated energy system optimization strategy based on particle swarm optimization algorithm

For the sake of increasing the energy efficiency of the city’s energy supply, it is an effective approach to adopt the joint planning of the integrated energy system employing a group of cooling heat and power. However, the coupling matrix in the integrated energy system is quite difficult to formulate. In this paper, a standardized construction method for modeling Energy Hub is proposed. The construction of the model is divided into several steps. In addition, the difficult of solving the coupling matrix of integrated energy system can be availably reduced through this method. Considering the nonlinear problem caused by dispatch factors, this paper proposes a comprehensive energy system optimization strategy by using improved particle swarm optimization algorithm. Finally, using an example of a residential area with cooling, heating and electricity system to verify the effectiveness of the proposed modeling and calculation method . At the same time, the influence of demand response and energy storage on economy during operation is analyzed.