Regional Integrated Energy System Research Articles

Short-term load forecasting of regional integrated energy system

Short-term load forecasting of regional integrated energy system

Research on multi-objective planning and optimization of integrated energy system based on economy and environmental protection

The development of a regional integrated energy system is an important way to improve energy utilization, alleviate the problem of renewable energy consumption and improve the environment. Aiming at the problems of single planning technology and fewer resource types in the existing integrated energy system planning, various types of equipment in the integrated energy system were built, and the integrated energy system was established with the goal of optimal economy and certain environmental protection. The system multi-objective planning optimization model, and then the particle swarm algorithm is used to solve the example problem to verify the feasibility and economy of the model and the optimization algorithm.

Study on the economic dispatch of regional integrated energy system based on master-slave game

Traditional demand response technologies optimize the load profile by cutting or shifting the electrical load, but they affect the customer’s energy experience, and thus the customer’s motivation to participate in demand response and load controllability are limited. For community integrated energy operators, the study of integrated customer demand response can explore their dispatch potential. Based on consumer psychology, we study the alternative energy use behavior of residential customers, establish a customer optimization model and a community integrated energy operator optimization model, and set up a Stackelberg master-slave game model with the operator as the leader and the customer as the follower. The simulation case verifies that the utilization of load-side multi-energy complementary characteristics and integrated demand response behavior can effectively improve the utilization of renewable energy and the efficiency of integrated energy operators.

Coordinated Planning of Electricity/gas/storage Distribution Network Based on LSTM and Demand Response

This paper presents a collaborative planning method of an electricity-gas-storage regional integrated energy system based on LSTM neural network and demand response. First, the LSTM Neural network is used for load forecasting, and the energy hub structure of the electric gas storage system is established. Then, the mathematical models of power storage, gas storage, electric network topology, gas network topology, and P2G are established to minimize the expansion cost of the electricity-gas-storage system, and the collaborative planning of energy storage, power lines, and natural gas pipelines is proposed based on the existing electric gas coupling integrated energy system. The original model which is difficult to solve is transformed into a mixed-integer linear programming model by introducing auxiliary variables, and the CPLEX solver is called to solve it. Finally, the economic advantages of collaborative planning of electricity-gas-storage system are verified by an example, and the connection of power storage and gas storage can reduce system pressure and optimize equipment selection.

Optimal Dispatch of Regional Integrated Energy System Based on a Generalized Energy Storage Model

In view of the difficulties in optimal dispatch caused by diverse energy and various types of equipment in the regional integrated energy system(RIES), this paper established a generalized energy storage model by extracting the flexibility characteristics of cooling, heat, and electrical load based on abstract equivalence. Instead of reducing the accuracy of regulation, this modeling method can greatly simplify the information interaction between the energy dispatching center and energy consumers, thus cutting down the time of collaborative analysis calculations. With the scheduling characteristics of multiple types of flexible loads in day-ahead and intra-day time scales considered comprehensively, a multi-time-scale optimization scheduling model of RIES is established, which takes into account the economy of system operation and the demand response. The simulation results show that the proposed optimization model can fully tap the load-side scheduling potential, along with effectively reducing the computational complexity. Through the coordination of resources with different characteristics, the pressure of balancing energy supply and demand can be relieved, and the operating cost of the system can be minimized.

A partition optimization design method for a regional integrated energy system based on a clustering algorithm

This study proposes a partition optimization design method that combines K-means and genetic algorithm (GA) for regional integrated energy systems (IESs) composed of complex loads. Building partitions are obtained by a multi-energy unified clustering model based on a K-means algorithm and performed for the electrical, heating, cooling, and gas loads of various buildings. The availability of all types of resources is evaluated by clustering the local resource data. According to the unified analysis of characteristics such as the thermoelectric ratio of loads and availability of resources, suitable energy supply equipment are selected to form an alternative structure set. Capacity configuration optimization models are established based on energy, economy, and environmental evaluation indicators considering the off-design performance of the equipment. The GA is used to optimize the configuration of each alternative structure for every partition. The system evaluation indicators are sorted by the linear weighting method to obtain an optimal system configuration suitable for each building partition. A case study is used to verify the effectiveness of the proposed method. The energy supply effect of the system designed for each partition is significantly improved compared with that of a separated production system. The proposed method has engineering significance for guiding the construction of regional IESs.

Optimal investment selection of regional integrated energy system under multiple strategic objectives portfolio

Investment decision-making is a critical factor to the success of regional integrated energy system (RIES) for an enterprise. Besides, the strategic objective of an enterprise has a great influence on the selection of the investment project. However, the problem as the investment selection of RIES project considering multiple strategic objectives portfolio has not been studied yet because of the massive difficulties in uncertain decision-making environment, various attributes of the object and diversity of the enterprise strategies. In order to solve this problem, this paper firstly comprehensively analyzes company strategies and summarizes them into three types. Then, an investment decision making framework of RIES project is established based on above three enterprises strategic objectives via IT2TrFNs-PROMETHEE-II model combining with a combined subjective and objective weight operator. Following that, robust analysis is implemented to test the reliability of the proposed decision-making framework. Finally, the superiority of the proposed RIES project investment decision making framework can be confirmed by a case study, and the result shows that enterprise’s strategic objectives will affect the optimal RIES project investment selection. This research has practical applied value for RIES investment decision makers in multi-strategic objectives portfolio management.

Adaptive clustering‐based hierarchical layout optimisation for large‐scale integrated energy systems

Different energy systems are generally planned and operated independently, which result in the low energy utilisation, weak self-healing ability and low system reliability. Therefore, an adaptive clustering-based hierarchical layout optimisation method is proposed for a large-scale integrated energy system, considering energy balance, transmission losses and construction costs. First, an adaptive clustering partition method based on energy balance and load moments is proposed to determine the optimal location of energy hubs and to allocate each distributed generation and load to different energy hubs, forming multiple regional integrated energy systems adaptively. Then, the proposed hierarchical layout optimisation model is formulated as to find the modified minimum spanning tree of the regional integrated energy system and multi-regional integrated energy systems, respectively, to construct an economical and reliable interconnection network. Finally, the effectiveness of the optimisation model and strategy is verified by simulations.

Coordinated planning of multi-energy systems considering demand side response

Integrated multi-energy systems, due to their flexibility and complementary characteristics, are considered an important energy system structure for accommodating high renewable penetration, and thus have attracted great attention of global research recently. Based on the energy hub, a two-stage optimal design and planning method for regional integrated energy systems (IES) is proposed in this paper, Furthermore, the demand side response (DSR) is taken into consideration. The EnergyPlus simulation software is used to produce multi-energy load data for various types of buildings in urban areas. Then, the improved k-means data clustering method and the Kolmogorov Distance scene reduction technology are applied to generate three types of typical scenarios. The objective of optimal IES planning is to minimize total capital cost, O&M cost, and the shifting load compensation cost while making sure the system operates reliably and safely. The column-and-constraint (CCG) generation algorithm is implemented to achieve fast optimization solution. The simulation results show the effectiveness of the proposed method, and the benefits of IES with complementary multi-energies are reflected

Multi-stage risk-averse operation of integrated electric power and natural gas systems

Regional electric power and natural gas systems have become increasingly interdependent in recent years, and have become subject to increasing supply and demand uncertainties associated with renewable energy sources. The present work addresses these issues by developing a multi-stage risk-averse operation model at the level of a regional integrated energy system that considers integrated demand response. Specifically, the developed multi-stage operation model can make flexible wait-and-see decisions, which are adaptive to newly observed uncertainties in the output of renewable energy sources, and the conditional value at risk approach is employed to achieve an economic operating cost at a given risk level. As a result, the multi-stage decision model outperforms conventional two-stage decision models in terms of operating costs. Test results indicate that the operation cost of the multi-stage operation model is reduced by 4.2% compared with a regular two-stage operation model. The impact of congestion in both the electricity and natural gas networks on system operations and energy prices is also investigated.

Energy Trading and Management Strategies in a Regional Integrated Energy System with Multiple Energy Carriers and Renewable-Energy Generation

With the development of energy markets and the introduction of renewable energy certificates (RECs), a regional integrated energy system (RIES) may participate in an energy wholesale market flexibly. In this paper, a bilevel optimization problem is formulated to determine the optimal management strategies of an RIES, including trading in the markets of electricity, natural gas, and RECs. In the bilevel model, an energy supplier (ES) purchases energy from energy wholesale markets and sets retail prices of energy products at the upper level to maximize its profit. At the lower level, various user aggregators (UAs) determine individual energy consumption strategies. The clearing prices of electricity, natural gas, and REC markets are determined asynchronously at the lower level. This nonlinear problem is then transformed into a mixed-integer linear programming problem using the incremental method and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are carried out to demonstrate the feasibility and effectiveness of the proposed method and the impacts of the RECs.

A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems

Regional integrated energy system (RIES) plays an important role in the energy economy because of its advantages such as low environmental pollution and high efficiency cascade energy utilization. In order to ensure the operational efficiency and reliability of RIES, the accurate prediction of energy demand has become a crucial task. To this end, this paper proposes a novel multi-energy load prediction model based on deep multi-task learning and ensemble approach for RIES. Its novelty lies in the following four aspects: (1) considering the high-dimensional temporal and spatial features, a hybrid network based on convolutional neural network (CNN) and gated recurrent unit (GRU) is utilized to extract high-dimensional abstract features and model nonlinear time series dynamically; (2) to meet the prediction requirements of various loads, three GRU networks with different structures are designed, which can adapt to different types of loads with various fluctuations; (3) considering the coupling relations, an enhanced multi-task learning with homoscedastic uncertainty (HUMTL) is proposed, which can better make the prediction tasks of various loads achieve the optimum simultaneously; (4) to realize the sharing of learning results of different structure networks, ensemble approach based on gradient boosting regressor tree (GBRT) is adopted, which can make a weighted summary by the prediction results of various energy features learning in different degrees. Numerical example shows that the proposed model can dig the coupling relations among various energy systems deeper, explore the temporal and spatial correlation of multi-energy loads further, and it has higher prediction accuracy and better prediction applicability than other current advanced models.

Optimization configuration of regional integrated energy system based on standard module

An optimal design of a regional integrated energy system (RIES) configuration is necessary to realize the efficient use of various energy resources as well to improve the energy efficiency and economic benefits of the integrated energy system (IES). Practical integrated energy projects often have problems with unreasonable capacity configuration which results in poor system economics and inoperability. In the current research on the capacity allocation of RIESs, the type of energy system is singular and the coupling relationship between various parts of the system is rarely considered. This results in a lack of theoretical guidance and calculation tools for the optimal designing of future RIESs. This study researches the optimization of the capacity allocation of IES based on typical residential area modules and typical commercial modules built on actual regional plots. TRNSYS is selected to simulate energy consumption, and Genopt has been selected to build the optimization system platform, couple the regional load demand, configure multi-energy system, and optimize the real-time results of the calculation. With aim to achieve the lowest life cycle cost of the IES, this research analyzes the optimized configuration of IESs including the composite cold and heat source system. This study establishes a fast and efficient IES optimal capacity ratio calculation model, which is 7.55% different from the actual energy consumption of the project. This study also provides theoretical methods and calculation tools for the ongoing research on the optimal design of RIESs, including composite cold and heat source systems and effectively improving system economics.

Regional integrated energy system dispatch strategy considering advanced adiabatic compressed air energy storage device

Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) has been considered to possess excellent potential of utilization in Regional Integrated Energy System (RIES) due to its various merits including superior ancillary service and multi-carrier energy generation and storage capacity. However, few studies have been reported on schedule strategies considering AA-CAES and its multiple applications in market environment. This paper presents a RIES schedule strategy considering multiple applications of AA-CAES accounting for trading strategy of Energy Hub Operator (EHO). The operation mechanism and the schedule framework of RIES are built at first. Then, key applications including heating service and reserve service of AA-CAES are analyzed and modeled, and the schedule model of RIES utilizing AA-CAES to optimize economy of EHO is developed. A RIES based on AC-DC hybrid micro-grid is used for case studies to assess the comprehensive benefits of AA-CAES for cogeneration, reserve providing and photovoltaic (PV) consumption. The results show that the multiple application capacity of AA-CAES lead to 21.89% increase in the overall profit of EHO. It improves the operation flexibility of RIES, optimizes market trading strategies of EHO and promotes the energy autonomy of RIES from various ways.

Demand response evaluation of RIES based on improved matter-element extension model

With the revolution of traditional economic and social patterns based on concentrated fossil energy intensive consumption, regional integrated energy systems (RIES) have gradually become an important trend for future energy consumption. As an important technical means of integrated energy demand side management, demand response helps to improve the smooth and reliable operation of the integrated energy system (IES). Meanwhile, it is of great significance to improve national energy security and the environment. This paper focuses on the demand response evaluation of RIES based on improved matter-element extension model. Firstly, with the introduction of the RIES demand response evaluation mechanism, the demand response index system of RIES is established from five dimensions: user load, user energy usage, user energy reliability, user energy consumption, and user energy habits. Based on the demand response evaluation index system, the potential demand response users of the integrated energy system are graded into four levels: rewarded user (I), encouraged user (II), general user (III), and non-encouraged user (IV).Secondly, based on the analytic hierarchy process and entropy method to obtain more accurate comprehensive weights, the RIES demand response evaluation model is established by the matter-element extension method. Finally, in order to illustrate the advantages of this method, 10 objects were selected from the regional integrated energy systems demand response project of a Chinese city for evaluation and analysis. The results show that the demand response abilities of the 10 evaluation objects are all at level I, indicating that the automatic demand response (ADR) of 10 evaluation objects is good.

Event-triggered online energy flow control strategy for regional integrated energy system using Lyapunov optimization

Regional Integrated Energy System (RIES) provides an inspiring perspective for constructing the future-oriented Energy Internet. However, the integration of heterogeneous energy system increases the complexity of critical power infrastructures, which limits the feasibility of energy management in real practice. To mitigate this issue, we propose an online optimization strategy for RIES with heating, ventilation and air conditioning (HVAC) loads in this paper. Specifically, a stochastic problem is formulated to minimize the economic cost with the consideration of power to gas and combined heat and power techniques. In addition, considering the changes of specific events. i.e., essential load, electricity price, queue length of renewable energy sources (RES) and comfort level of end-users, an event-triggered mechanism is established to enable automatic execution of scheduling signals. Without relying on a priori knowledge, we use the Lyapunov optimization method to control energy queue of HVAC and the system stability in real time. The feasibility of the proposed online energy flow control algorithm has been validated by the theoretical deduction. The numeral simulation based on real-world traces demonstrates that the proposed method maximizes the economic profit by incenting the load transferring during peak period of electricity price and effectively improves the utilization ratio of RES. Moreover, this method can ensure the end-user’s comfort while dramatically reducing the dispatching frequency of HVAC with the event-triggered mechanism.

Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things

To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China.

Research on optimal allocation strategy of multiple energy storage in regional integrated energy system based on operation benefit increment

Wind power generation curve generally has the reverse characteristic with the peak regulation of electricity load curve, which will lead to serious wind abandonment and energy waste. The development of integrated energy systems and energy storage maybe change this predicament. Based on the infrastructure and operation model of regional integrated energy system, this paper studied the allocation priority of cooling, heating, electricity storage and electricity to gas (P2G) system, considering multiple energy complementary and collaborative operation of combined cooling heating and power (CCHP) units and other equipment. Firstly, the operation benefit increment index was proposed to quantify the operation cost variation, also named promotion effect in this paper, between the scenarios of integrated energy system without/with certain form energy storage added. Then, an configuration optimization model of multiple energy storage devices based on operation benefit increment was put forward. By employing an industrial district integrated energy system in northern China as the study case, the operation scheduling scheme and the priority of energy storage configuration were solved by the optimization strategy proposed in this paper. And the promotion effects of different energy storage configurations were analyzed from the viewpoint of operation economy of the integrated energy system. The results show that the abandoned wind power and the cost of environmental pollution control are reduced in the integrated energy system with four kinds of energy storage. And the economic efficiency is greatly improved.

Research on capacity planning and optimization of regional integrated energy system based on hybrid energy storage system

Some regional integrated energy systems (RIES) have installed equipments such as wind turbine and photovoltaic, but the fluctuation of these intermittent power supply is large, resulting in a certain amount of energy waste. Because energy storage can improve the utilization rate of renewable energy, this paper establishes a storage capacity expansion planning model considering multiple functions of hybrid energy storage in the RIES. In this model, the wavelet packet decomposition method is used to reasonably decompose the high-frequency fluctuation and low-frequency fluctuation of renewable energy to achieve the function of stabilizing fluctuation. Meanwhile, an operation strategy for hybrid energy storage to participate in demand response is proposed. Rain flow counting method is used to research the life of hybrid energy storage system, which improves the accuracy of energy storage annual cost calculation. In the Simulation and Analysis section, the genetic quantum algorithm is used for simulation and analysis of the role and benefits of hybrid energy storage. The simulation results show that the benefit of hybrid energy storage in capacity expansion construction is increased by 10.4%, and when the electricity and gas prices fluctuate by ±20%, the hybrid energy storage configuration proposed in this paper has a stable advantage in more than 95%.

A decentralized multi-energy resources aggregation strategy based on bi-level interactive transactions of virtual energy plant

Under the integrated energy system, user-side resources have strong flexibilities and the number of resources is huge. It is challenging to aggregate user-side decentralized multi-energy resources to participate in integrated energy market transactions. The threshold for the transaction access is an important factor in the low participation rate of decentralized multi-energy resources on the user side for the trading and operation of integrated energy systems. To this end, this paper builds an operating framework of the grading dynamic aggregation platform, and proposes a decentralized multi-energy resources aggregation strategy based on bi-level interactive transactions of virtual energy plant (VEP). First, we put forward the operation model of VEP, and analysis the market influencing factors of VEP in bi-level interactive transactions. Secondly, a decentralized multi-energy resources grading dynamic aggregation model based on VEP is established and applied to the VEP optimal scheduling model. Finally, taking a regional integrated energy system as an example, including electricity, gas and heat systems. The results show that the proposed method can dynamically aggregate decentralized multi-energy resources that do not meet the entry rules to participate in system transactions and operations. Compared with the fixed aggregation method, the decentralized resource utilization rate is increased by 25.87%. Moreover, the proposed model effectively improves the bidding efficiency of the VEP and the economic benefits are increased by 6.52%.