Planning Of Integrated Energy System Research Articles

An enhanced stochastic optimization for more flexibility on integrated energy system with flexible loads and a high penetration level of renewables

Full utilization of the potential regulation ability of flexible loads in an integrated energy system (IES) and expanding its structure for more flexibility via integrating auxiliary devices is the key to realizing its high-efficiency operation and high-proportion renewable consumption. The present paper builds the flexible regulation models of thermal and electrical loads and introduces them to the structure expansion planning of IES. A two-stage stochastic probability optimization method integrating the uncertain operation of introducing flexible loads is then proposed to balance the additional costs of device integration and the benefits of performance promotion. Herein, an enhanced sample average approximation based on a stochastic hierarchy scenario generation method is developed to solve the optimization. The regulation mechanisms of flexible loads and their influence on expansion planning are then analyzed by comparing the optimized results. The results show that the path to decreasing the cost via introducing flexible thermal and electrical loads into optimization is increasing renewable consumption and coordinating an increment in renewable consumption and average electrical efficiency, respectively. The regulation between them has synergism on cost reduction and saturation on increasing renewable consumption. The synergism can reduce the cost by 0.62 %. Besides, there is a synergism between electric boiler integration and flexible loads, which further reduces the total cost of the expanded IES by 24.86 %.

Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied.

Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method.

Data-driven Evaluation Method for Cyber-Physical System Reliability of Integrated Energy System

Background:: In order to achieve the social goal of low carbon and high efficiency, integrated energy system (IES) with deep integration of cyber-physical systems (CPS) are being developed rapidly. The reliability evaluation is an important basis for IES planning and operation. Considering the deep coupling relationship between various systems, energy networks, and cyber networks, and the impact of multiple uncertainties on the integrated energy system, the traditional single index is difficult to quantify the reliability of the system, and the current evaluation methods rely on mechanism analysis and accurate modeling. It is difficult to deal with the coupling relationship between integrated energy systems, energy networks and information networks. So, the quantitative evaluation of the reliability level index of integrated energy systems under the cyber physical system is established, and a data-driven evaluation method is proposed. Methods:: In this article, the energy entropy and information entropy theory are combined to establish the reliability evaluation index of integrated energy system integration of cyber-physical systems, and a data-driven evaluation method based on random matrix theory (RMT) is proposed. Results:: A reliability evaluation model is established through RMT, and the single-ring law is used to analyze the statistical properties of the matrix model to identify the abnormal state of the system. Then the reliability of cyber and physical layers are evaluated based on energy entropy and information entropy, respectively. In addition, sensitivity analyses on the impact of node interruption probability, network topology, access load rate, and other factors are carried out. Conclusion:: The examples of IES are given to verify the feasibility and effectiveness of the proposed method.

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.

Stochastic planning of integrated energy system based on correlation scenario generation method via Copula function considering multiple uncertainties in renewable energy sources and demands

AbstractThe main goal of integrated energy service providers (IESP) in the future is to pursue profitability and reduce carbon emissions while ensuring sufficient energy supply. However, the multiple uncertainties associated with renewable energy resources and the multi‐energy consumers pose significant challenges in the optimal planning of the integrated energy system (IES) based on energy hubs (EH). This includes the global optimization of device types, EH capacity, and the optional interconnections between adjacent EHs. This study aims to develop a comprehensive mathematical model of EH that considers the temperature limitations on device operation. The thermal bus and associated devices are divided into high and low temperature parts, and a multi‐scenario stochastic programming model is proposed for IES planning. Scenarios are generated using non‐parametric kernel density estimation and the Copula function, a scenario reduction technique is implemented to alleviate computational burden. Additionally, case studies illustrate effectiveness of the proposed approach, the operation and forms of EHs, effect of the interconnections and the trade‐off between carbon emissions and economic costs are further studied by extensive simulations. This study can be a good guide for IESPs to invest in the construction of the EH‐based IES.

Optimization of integrated energy system for low-carbon community considering the feasibility and application limitation

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community.

Research on Optimization Method of Integrated Energy System Network Planning

The development of an integrated energy system (IES) is conducive to promoting the transformation of the energy system and helping to achieve the ‘double carbon’ goal in China. The IES integrates cooling, heating, electricity, gas, and other energy resources, which is significantly more difficult than single energy network planning. This paper systematically sorts out the process of IES network planning and proposes an improved methodology. Firstly, the bottom-up dynamic multiple-load forecasting method of 8760 h a year is proposed as the basis of system configuration and capacity selection. Subsequently, a planning method for energy station location and route optimization using the Dijkstra algorithm is constructed by applying the P-median optimization model. Finally, when optimizing the capacity allocation of the IES, the complementary characteristics of natural gas, electricity and heat, as well as the corresponding energy demand characteristic, have been fully considered, so that the optimization objectives can be reasonably determined. Through the actual calculation, it is found that the optimization method proposed in this paper can reduce the construction cost of the network by 41%. The work combines the process of energy network planning and capacity configuration of IES, which provides a simple, easy and economical solution for IES planning in new areas.

IESP: An integrated energy system planning platform

Climate change caused by increasing greenhouse gases emissions challenges power system to transit to a low-carbon one. Integrated energy system gathering with various energy form of electricity/gas/heat/cold is considered to be a viable solution. Planning for integrated energy systems to realize high-efficiency energy utilization and carbon emission reduction is of great significance. For fast and direct learn about how integrated energy system planning is performed, a platform for planning integrated energy systems is developed in this paper, namely IESP (Integrated Energy System Planning). Different modeling modules are illustrated step by step to help relevant practitioners to better understand how planning schemes for integrated energy systems are determined from scratch.

Multi-stage time sequence planning model of integrated energy system considering extreme scenarios and uncertainty

Integrated Energy System (IES) is far-reaching significance for improving energy utilization efficiency and reducing carbon dioxide emissions. However, due to the lack of consideration of construction sequencing, carbon trading development trends and uncertainty risks in the current IES planning scheme, problems such as mismatch between supply and demand, excess carbon emissions and weak renewable energy carrying capacity have emerged. To address these problems, this study proposes a multi-stage time sequence robust planning model for IES that considers carbon trading and extreme scenarios. First, the synergistic and complementary energy framework of IES is introduced, and the operation principle of each unit in the system is briefly shown by matrix. Then, considering the construction sequence of multiple stages and the load difference in different seasons, a life cycle planning model is established, which includes carbon trading cost, renewable energy curtailment penalty cost, integrated demand response cost. In order to improve the system’s ability to withstand the uncertainty of power and load, the extreme scenario is obtained according to the confidence level, and the improved robust optimization model is used to enhance the robustness of the system, which plays a dual role of risk prevention and control. Finally, a green ecological park is used as the object to implement simulation, and a safer planning scheme is obtained under extreme scenarios. The case study shows that: (1) Compared with the single-stage planning method, the method proposed in this study can reduce the depreciation cost in the whole life cycle by 5.44%, and improve the phenomenon that the curtailment rate of wind power exceeds 20%. (2) After participating in the carbon market, the cumulative income generated in the three stages is 17.92 million ¥. (3) After implementing demand response, the cumulative configuration capacity of wind turbine, ground source heat pump and energy storage decreased by 1.02%, 7.85% and 57.6%, respectively. (4) The improved robust optimization model improves the risk resistance of the system.

Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

Bi-level optimal configuration strategy of community integrated energy system with coordinated planning and operation

Nowadays, the integrated energy system (IES) has become a hot topic in the field of energy research. In this paper, a bi-level optimal configuration strategy is proposed for a community integrated energy system (CIES), which is based on energy supply-demand responses and robustness adjustable scenarios. First, from the perspective of energy supply and demand, various energy equipment on the community side and aggregated energy loads on the user side are analysed and qualitatively modeled, and a multi energy supply-demand of CIES is established. Next, according to the robustness requirements of IES planning, the scenario sampling, sorting, screening and reduction are performed subsequently to obtain a typical set of robustness adjustable scenarios. On this basis, a bi-level optimal configuration model that coordinates the aggregated configuration and operation is developed to design the CIES. While the upper-level model takes the lowest total annual cost as the goal to configure the quantity and capacity of energy equipment, the lower-level model optimizes the scheduling scheme with the aim of the best operation economy under typical scenarios. Finally, case studies are carried out based on a practical town area, and simulation results show the effectiveness and advantage of the proposed strategy.

A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the proposed method’s effectiveness in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified.

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between Uncertainties

In this paper, a novel methodological framework for energy hub (EH) planning, considering the correlation between renewable energy source (RES) and demand response (DR) uncertainties, is proposed. Unlike other existing works, our study explicitly considers the potential correlation between the uncertainty of integrated energy system operations (i.e., wind speed, light intensity, and demand response). Firstly, an EH single-objective interval optimization model is established, which aims at minimizing investment and operation costs. The model fully considers the correlation between various uncertain parameters. Secondly, the correlation between uncertainties is dealt with by the interval models of multidimensional parallelism and affine coordinate transformation, which are transformed into a deterministic optimization problem by the interval order relationship and probability algorithm, and then solved by a genetic algorithm. Finally, an experimental case is analyzed, and the results show that the research method in this paper has good engineering practicability. At the same time, different correlations among uncertainties have different influences on integrated energy system planning. Correlation and influence are positively correlated.

Review on Coordinated Planning of Source-Network-Load-Storage for Integrated Energy Systems

The integration of electricity, gas, and heat (cold) in the integrated energy system (IES) breaks the limitation of every single energy source, which is the development trend of future energy systems. To realize the coordinated planning of “source-network-load-storage,” the IES has to be conducive to improving energy efficiency, bringing economic and environmental benefit, and achieving sustainable development of energy. In this paper, the techniques and methods involved in IES planning are summarized. First, the structure and characteristics of the IES are briefly introduced. Second, the key findings of the IES planning are summarized from four perspectives: source, network, load, and storage. Then, the modeling methods of the IES collaborative planning are summarized, and the optimization methods for solving complex planning problems are analyzed. Compared with previous reviews, this paper focuses on the modeling of multi-energy coupling of each part of source-network-load-storage and modeling of the overall collaborative planning. Finally, the future research direction of IES planning is forecast.

Data-Driven Multi-Energy Investment and Management Under Earthquakes

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

Energy stations and pipe network collaborative planning of integrated energy system based on load complementary characteristics

Abstract In view of the lack of effective energy station site optimization method in the existing integrated energy system (IES) planning, and the failure to consider the load characteristics in the division process of the energy supply area of energy stations, a collaborative planning method of energy stations and pipe networks considering the complementary load characteristics is proposed. Firstly, based on the analysis of the interaction between the station and network planning of IES, an alternated optimal method framework of energy station site, energy supply area division and pipe network layout are proposed. Secondly, based on the energy supply radius of the energy station, this paper proposes a method to determine the number of new energy stations, and based on the topological characteristics of the cellular networks, a method of determining the initial energy station site is proposed. Thirdly, considering the complementary load characteristics, the evaluation index and division method of energy supply area of integrated energy station are proposed. Fourthly, aiming at the problem of pipe network layout on the road, a pipe network optimization method based on Kruskal algorithm is proposed, and optimizes the direction of load access to the pipe network by the linear optimization model. Fifthly, the calculation method of equivalent energy distance of load and the optimization method of energy station site based on equivalent energy distance are proposed. Finally, the rationality and effectiveness of this method are verified by an example.

Optimal Planning of Islanded Integrated Energy System With Solar-Biogas Energy Supply

Renewable-based integrated energy system (IES) is considered a viable energy supply alternative in many off-grid and remote regions of the world. However, high costs of islanded IESs with large-size batteries challenge further deployments of IESs. Considering the variable nature of renewable energy and distinctive characteristics of abundant biomass energy in remote regions, this article proposes a distributed solar-biogas residential IES, which can supply thermal, electrical and gas loads in remote locations, and utilize the complementarity nature of solar and biogas energy for reducing the dependency of IES on battery storage system. The proposed IES planning model, formulated as a two-stage MILP problem, is solved by the Benders Decomposition (BD) method to determine the optimal capacity of each component in IES planning. According to the proposed sensitivity analyses, load levels and feedstock prices have a substantial influence on investment decisions and total cost. The article also incorporates the thermodynamic effect of temperature on anaerobic digestions and illustrates the complementary economic and technical merits of solar and biogas energy in daily IES operations. Illustrative examples show the corresponding IES advantages on investment cost reduction and peak load shaving.

Energy station and distribution network collaborative planning of integrated energy system based on operation optimization and demand response

In the energy station and distribution network collaborative planning of integrated energy system (IES), it is difficult to consider station and network interaction in IES operation at the same time. For resolving this problem, the model and solution of planning and operation alternative optimization are proposed considering multiagent interest balance. First, the three-stage optimization framework and model of station and network planning of IES are proposed based on the operation scheduling of IES and the response of energy station and users as different subjects, aiming at optimization of station and network cost-effectiveness, respectively. Second, the distribution network simplification method is proposed based on topological equivalence principle. The topological structure characteristics of distribution network is analyzed, the rapid generation strategy of single tie line network based on broken circle method is proposed, and then an encoding/decoding scheme is proposed based on particle swarm optimization algorithm. Third, considering the load characteristics and energy station demand response based on flexible comfort level of users, a dynamic spot price optimization method and operation strategy of IES are proposed. And then the station and network collaborative planning solution is presented based on the operation optimization of IES. Finally, an example is given to verify the practicability and effectiveness of the proposed method in this paper.

Multi-objective planning for integrated energy systems considering both exergy efficiency and economy

Improving the comprehensive utilization level of energy has been a core goal for the optimal design of the integrated energy system (IES), which requires an accurate assessment of energy efficiency. The widely used criterion of conventional energy efficiency derived from the first law of thermodynamics has gradually been criticized for its shortcomings of neglecting the quality of energy. As a concept considering both the quantity and quality of energy, exergy efficiency has been recognized as a more reasonable index in recent decades. Nevertheless, it is a complicated problem to calculate the exergy efficiency among multiple energy forms in an electricity-gas-heating-cooling coupled IES, limiting its application in IES planning. This paper hence proposes the method of calculating the exergy efficiency of an IES by using a black-box model, after defining the concept of energy quality coefficient (EQC) that values the quality of various energy forms involved. And then, a multi-objective planning model considering both exergy efficiency and economy is thus proposed for the joint planning of energy generators, storages and networks, wherein a five-tier energy hub is modeled to deal with the multi-energy couplings. To reduce the difficulty of optimizing, several convex relaxation measures are designed to transform the non-convex original problem into a convex one. The proposed framework is verified in a real-world study case located in Guangzhou, China.