Integrated Energy System Model Research Articles

Balancing the books: unveiling the direct impact of an integrated energy system model on industries, households and government revenues

BackgroundThe transition towards a sustainable energy system is reshaping the demand for final energy, driven by the diffusion of new end-use technologies. This shift not only impacts consumers’ energy expenses, but also holds implications for the public budget. Building on data from a German energy transition scenario, we analyse the direct impact of energy costs on industries, low-income households, and changes in government revenues from the taxes and levies on final energy carriers. Our analysis considers the impact of current policies and explores a scenario introducing additional excise tax rates to offset potential revenue losses.ResultsWe found that substantial carbon price increases could generate revenues that offset the losses from excise taxes on fossil fuels while enabling the financing of renewable support from the public budget by the end of this decade. Nevertheless, a decline in government revenues from taxes and levies is anticipated after 2030 until the middle of the century due to the declining use of fossil fuels. Maintaining current excise tax revenues during the transition could be achieved by introducing additional excise taxes on fossil fuels and electricity. Lastly, our analysis indicated a continuous decline in household energy expenditures until 2050, whereas energy-intensive industries face adverse impacts due to decarbonisation.ConclusionsThis research provides valuable insights into the fiscal implications of the energy transition, shedding light on different industrial sectors and households while considering the evolving impact on the public budget. Policymakers may need to consider systemic reforms or alternative financing mechanisms outside the energy system to balance the books.

A novel controllable bias reinforcement learning method for distributed automatic generation control integrated with large-scale electric vehicles

Due to the introduction of large-scale renewable energy resources and flexible load access, strong random disturbances will bring to the current integrated energy system. The traditional control methods fall short in adequately addressing the frequency regulation needs of distributed systems. From the perspective of distributed automatic generation control (AGC), an optimal coordinated control method is proposed based on reinforcement learning. The proposed method is developed to mitigate the estimation bias of the action-values via the minimum action-value from multiple action-value estimates. Further, the bias of action-value estimates can be adjusted from positive to negative to enhance the control accuracy. Then this method uses behaviour utility trace to achieve adaptive adjustment of the exploration rate, preventing repetitive and unbalanced exploration. Simulation experiments have been performed on the two-area LFC model integrated with electric vehicles and the multi-area integrated energy system model to verify the effectiveness of the proposed control method.

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity–thermal–gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, heat, and gas decreased by 39.9%, 90.5%, and 74.2%, respectively, effectively improving the economic viability of the system.

Day-Ahead scheduling of rural integrated energy systems based on distributionally robust optimization theory

The integration of diverse industries in rural areas is an important step in building a smart countryside and achieving rural revitalization. Aiming at the problem of high energy consumption and pollution in agricultural production parks under the traditional energy structure, a new rural comprehensive energy park distribution robust scheduling model for water containing product breeding greenhouses is proposed in this paper. Firstly, a greenhouse model for aquaculture based on the principle of thermal flow is established. Secondly, considering energy production, consumption and storage, a rural integrated energy system model is established. In response to the uncertainty of residential electricity load, solar radiation, and environmental temperature within the park, a distributed robust optimization model is established using Kullback Leibler divergence. Finally, a numerical simulation is conducted using energy data from a real rural aquaculture park. The results show that compared to agricultural production parks under traditional energy models, the new rural comprehensive energy system model proposed in this paper fully integrates rural resources, reduces daily operating costs by 74.832 %, and reduces total carbon emissions by 33.75 %, providing theoretical support for rural industrial structure adjustment and energy transformation.

Integrated energy system model with multi-time scale optimal dispatch method based on a demand response mechanism

A flexible integrated energy system (IES) can curb the supply–demand imbalances caused by renewable energy and load uncertainty. This study proposes an IES model based on a demand response mechanism and a multi-time-scale optimization scheduling method with the aim of fully utilizing its flexibility. Specifically, the demand response strategy of electric heating/cold complementary substitution and horizontal time shifting was introduced at different stages of dispatch to enhance the flexible dispatching capability of the IES. Furthermore, the contribution of thermal inertia of pipes in resisting supply and demand uncertainties and reducing thermoelectric coupling was considered. On this basis, the time resolution was determined according to the adjustment rates of the different components, and a multi-timescale optimal scheduling method was developed. The quantum sparrow search algorithm was applied to solve the proposed model, and the optimal scheduling strategy of the system was obtained. Results revealed that, from an environmental perspective, the amount of wind and light abandonment was reduced by 89.71%, thereby increasing the penetration of renewable energy and promoting carbon neutrality. From an economic perspective, the daily operating cost decreased by 1380 yuan/day, effectively improving the operational costs of the IES. From a reliability perspective, thermal inertia of networks reduced the intensity of thermoelectric coupling and increased the flexibility of IES dispatching, curbing the uncertainties in renewable energy and loads during system operation.

Study on optimal allocation of energy storage in multi-regional integrated energy system considering stepped carbon trading

Abstract In this study, an energy storage configuration optimization model of multi regional integrated energy system based on integrated scheduling and stepped Carbon emission trading is proposed. By analyzing the steady-state, dynamic, and variable operating characteristics of multi regional integrated energy systems, a distributed energy planning modeling method was adopted to construct energy storage configuration constraint objects and optimization models. The target scheme of energy storage configuration is optimized by using the results of integrated scheduling scheme and dynamic distribution analysis of ladder Carbon emission trading, and the parameters are optimally estimated by using Monte Carlo method and quadratic fitting algorithm. The simulation results show that this method has a strong scheduling capability in the context of stepped Carbon emission trading, can accurately assess the load demand and supply demand relationship, reduce carbon emissions, and improve energy efficiency, with an average of 0.9121.

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

AbstractTo accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network.

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Optimal Dispatch of Integrated Energy Systems Considering Advanced Adiabatic Compressed Air Energy Storage with a High Percentage of Fluctuating Energy Consumption

Aiming at the integrated energy system (IES) of the region with a multi-energy coupling of cold, heat, and power (CHP), this research establishes the IES model with the advanced adiabatic compressed air energy storage (AA-CAES) as the energy hub. Further, to increasingly improve the system’s consumption ratio of fluctuating energy sources such as wind power, this paper designs the AA-CAES wide operating condition operation scheme based on the compressor adjustable inlet guide vanes (VIGVs) regulation technology. Finally, it is validated with a real application Case. Simulation results indicate that the operating costs of the newly modeled IES can be decreased by 20% compared to a regular integrated energy system, which demonstrates the effectiveness of AA-CAES-wide operating conditions in promoting the economic operation of integrated energy systems (IES).

A Multi-Agent Integrated Energy Trading Strategy Based on Carbon Emission/Green Certificate Equivalence Interaction

To meet the demand for constructing a market mechanism that adapts to the integrated energy system and promotes market-oriented reforms in the energy sector, in-depth research on integrated energy trading strategies is required. This study focused on the integrated energy trading problem and clarify the relationships among participants in the integrated energy market. A regional integrated energy system model was established that enables trading of electricity, gas, heat, and cold, and propose a integrated energy trading strategy based on the carbon emissions/green certificate equivalence interaction. Firstly, the trading process of carbon emissions and green certificates, the underlying representation of green attributes, and market transaction prices are analyzed. Combining with a tiered carbon trading system that includes rewards and penalties, a carbon emissions/green certificate equivalence interaction mechanism is constructed. Secondly, the paper utilized the flexible characteristics of loads within the industrial park to establish a integrated energy demand response model for electricity, heat, and cold. Finally, with the objective of minimizing regional operating costs, a integrated energy trading model considering the carbon emissions/green certificate equivalence interaction mechanism was developed. In the simulation, the operating cost of the system is reduced by 4%, and the carbon emission is reduced by 11.4%, which verifies the effectiveness of the model.

Research on joint planning method of supply and demand considering users’ comprehensive energy consumption behavior and policy orientation

Given the recent escalation of the energy and environmental crises, it is crucial to consider the evolution of various energy systems from a macro perspective in order to increase the overall efficiency of energy use and improve the environment. In order to tackle this issue, a wide-area integrated energy system planning method that takes into account consumers' comprehensive energy consumption behavior (CECB) and policy recommendations is provided in this study. First of all, according to the analysis of users’ CECB, the decision-making model of regional users' energy supply transformation is established. A game model is constructed to explore cooperative planning of an integrated electricity-natural gas energy system based on the profit models of various market participants. The government is considered as the main body of planning and decision-making, and the evaluation model of government’s pollution emissions from both supply and demand sides is established. A two-level dynamic game planning model for the wide-area IES is then created as a result of the examination of the game mechanism between users, the government, the integrated energy system (IES), and other market players. Finally, the model is solved using an iterative search approach. Results from simulations are used to validate the suggested method’s accuracy and efficacy.

Research on Optimization and Scheduling of Multi-Integrated Energy System Based on Step-by-Step Carbon Trading and Hybrid Games

In light of the current integrated energy system (IES) transactions, the interaction between the supply and demand sides is not fully considered. With the overall development of the carbon trading market, there is a need to deeply explore the interests of multiple entities in energy trading under the step-by-step carbon trading mechanism. Therefore, research on the optimization and scheduling of multicomprehensive energy systems based on step-by-step carbon trading and hybrid games is proposed. Firstly, the interests and demands of various integrated energy systems and users in multi-integrated energy system energy trading should be comprehensively considered. Based on the master-slave game and Nash bargaining theory, an optimization model of multiple integrated energy systems is built under the step-by-step carbon trading mechanism and load aggregation, representing the interests of the entire user side. Secondly, the proposed game optimization model has been established to prove it can maximize social benefits. IES and user game optimization models have been established, with multi-IES being leaders with the goal of maximizing their own benefits, guiding the optimization between IES and load by formulating energy prices. Users are followers who aim to maximize comprehensive benefits and respond to IES’s decisions through cooperation. Finally, using the improved gray wolf algorithm to solve the built model, it has been proven through comparison of calculation examples that the proposed method can reduce carbon emissions, effectively coordinate the optimization scheduling of multi-IES, and achieve the fair distribution of multi-IES cooperation benefits. This improves the effectiveness of individual and social benefits.

Impact of Carbon Capture (Storage) and Carbon Tax on Economic Dispatch of an Integrated Energy System

Finding the balance between economy and low emission of an integrated energy system (IES) has become one of the current research hotspots. This article introduces the carbon tax and carbon capture (storage) technology within the framework of the IES model. The IES comprises of components such as combined heat and power units equipped with carbon capture facilities, wind power generators, photovoltaic panels, and energy storage systems. The objective function of the model is the minimization of operational costs. Low-carbon economic operation dispatching problem under the constraints of energy conversion, energy balance, and operation cost is studied based on a mix-integer linear programming model. Through sensitivity analysis, this study explores the impact of varying carbon tax levels on the operational costs and emissions of an IES while considering peak and valley price differences. The emission reduction potential of the IES under different policy and technology scenarios is also estimated.

Optimal planning of integrated energy system considering swapping station and carbon capture power system

An optimal planning method for an integrated energy system (IES) considering electric vehicles (EVs) swapping station (SS) and carbon capture power system (CCPS) is studied in this paper. Firstly, based on the analysis of swapping EV load, the model of SS is established. Then, we built the models of CCPS, and other energy supply and storage equipment in IES. Based on this, the annual comprehensive cost of planning is regarded as the optimization objective, a planning model of IES considering SS and CCPS is developed, and solved by CPLEX. Finally, an actual case was analyzed to verify the feasibility and effectiveness of the planning method proposed in this paper.

Optimal configuration of a two-level integrated rural energy system for counting and biogas digester warming

This paper proposes a strategy to use wind power (WP) that cannot be fully consumed at the load side for biogas digester (BD) heat augmentation to improve the utilization rate of biomass while promoting the consumption of WP, in view of the anti-peak nature of WP and the low gas production of BD at night in winter. In this paper, based on the model of rural integrated energy system (RIES) with biogas digester heating, a double-layer optimization model with minimum investment cost in the upper layer and optimal operation in the lower layer is developed. And then the Karush–Kuhn–Tucker (KKT) condition is used to convert the lower layer model into the constraints of the upper layer model, The Big-M method is used to linearize the nonlinear problem, and the model is transformed into a single-layer mixed integer linearization model. Finally, a use case of the algorithm is established and the CPLEX solver is called to solve the problem.

Design, modeling and simulation of nuclear-powered integrated energy systems with cascaded heating applications

Nuclear-renewable integrated energy systems (IES) consist of a variety of energy generation and conversion technologies and can be used to meet heterogeneous end uses (e.g., electricity, heat, and cooling demands). In addition to supply-demand balance, end-use heat demands usually require heat supply of certain temperature ranges. The effective and efficient utilization of heat produced within an IES is, therefore, a critical challenge. This paper examines design options of an IES that includes heating processes of multiple temperature grades. We investigate a cascaded design configuration, where the remaining residual heat after high-grade heating processes [e.g., hydrogen production through high-temperature steam electrolysis (HTSE)] is recovered to meet the low-grade heating needs [e.g., district heating (DH)]. Additionally, a thermal energy storage system is integrated into the DH system to address the imbalance between heat supply and demand. This paper primarily focuses on the design and modeling of the proposed system and evaluates its operation with a 24-h transient process simulation using a DH demand profile with hourly resolution. The results indicate that the residual heat from the HTSE exhaust is insufficient for the DH demand, and additional topping heat directly from the reactor process steam is needed. Furthermore, the inclusion of thermal energy storage within the DH system provides the necessary balance between thermal generation and demand, thereby ensuring a consistent rated temperature of the DH supply water. This approach helps minimize the control actions needed on the reactor side.

Low-carbon economy dispatching of integrated energy system with P2G-HGT coupling wind power absorption based on stepped Carbon emission trading

To improve the renewable energy consumption capacity of integrated energy system (IES) and reduce the carbon emission level of the system, a low-carbon economic dispatch model of IES with coupled power-to-gas (P2G) and hydrogen-doped gas units (HGT) under the stepped carbon trading mechanism is proposed. On the premise of wind power output uncertainty, the operating characteristics of the coupled electricity-to-gas equipment in the system are used to improve the wind abandonment problem of IES and increase its renewable energy consumption capacity; HGT is introduced to replace the traditional combustion engine for energy supply, and on the basis of refined P2G, a part of the volume fraction of hydrogen obtained from the production is extracted and mixed with methane to form a gas mixture for HGT combustion, so as to improve the low-carbon economy of the system. The ladder type carbon trading mechanism is introduced into IES to guide the system to control carbon emission behavior and reduce the carbon emission level of IES. Based on this, an optimal dispatching strategy is constructed with the economic goal of minimizing the sum of system operation cost, wind abandonment cost, carbon trading cost and energy purchase cost. After linearization of the established model and comparison analysis by setting different scenarios, the wind power utilization rate of the proposed model is increased by 24.5%, and the wind abandonment cost and CO2 emission are reduced by 86.3% and 10.5%, respectively, compared with the traditional IES system, which achieves the improvement of renewable energy consumption level and low carbon economy.

Low-carbon economic operation of IES based on life cycle method and hydrogen energy utilization

The Integrated Energy System (IES) that coordinates multiple energy sources can effectively improve energy utilization and is of great significance to achieving energy conservation and emission reduction goals. In this context, a low-carbon and economic dispatch model for IES is proposed. Firstly, a hydrogen energy-based IES (H2-IES) is constructed to refine the utilization process of hydrogen energy. Secondly, the carbon emissions of different energy chains throughout their life cycle are analyzed using the life cycle assessment method (LCA), and the carbon emissions of the entire energy supply and demand chain are considered. Finally, a staged carbon trading mechanism is adopted to promote energy conservation and emission reduction. Based on this, an IES low-carbon and economic dispatch model is constructed with the optimization goal of minimizing the sum of carbon trading costs, energy procurement costs, and hydrogen sales revenue, while considering network constraints and constraints on key equipment. By analyzing the model under different scenarios, the introduction of life cycle assessment, staged carbon trading, and hydrogen energy utilization is shown to promote low-carbon and economic development of the comprehensive energy system.

Integrated energy system planning considering renewable energy uncertainties based on multi-scenario confidence gap decision

The problem of renewable energy uncertainties in the capacity planning of integrated energy system (IES) is prominent. To handle the multiple uncertainties, multi-scenario clustering analysis and classified confidence intervals of Gaussian mixture model (GMM) are combined, along with the robustness idea of information gap decision theory (IGDT), so a novel multi-scenario confidence gap decision theory (MCGDT) is proposed. Considering the comprehensive optimization objectives of maximizing exergy efficiency and minimizing annualized total cost, a robust capacity planning model for IES based on MCGDT is constructed to promote the cascade utilization of multiple energy and the economic performance of IES. Moreover, a new cross entropy-radar scanning differential evolution (CE-RSDE) algorithm is designed to improve solution efficiency and avoid prematurity in the optimization process. The simulation results of a typical case suggest that the proposed method leads to better capacity planning of IES, with up to 7.55% reduction of the annualized total cost and 14.03% increase in the exergy efficiency, while keeping strong robustness under the environment of multiple uncertainties.