Integrated Energy System Model Research Articles

Distributed robust synergistic scheduling of electricity, natural gas, heating and cooling systems via alternating direction method of multipliers

Due to the development of multi-energy conversion technologies and the higher public awareness of the sustainability, the energy system has begun a transition toward the energy hub (EH) based integrated energy system (IES). This paper proposes a distributed robust synergistic scheduling method for multi-EH-based IES coupled with electricity, natural gas, heating and cooling, to minimize the operating cost of the IES while considering the uncertainties of renewable generations (RGs) and multi-energy load demands. The nonconvexity of energy network constraints, uncertainties in multiple EHs as well as limitation of information exchange lead to significant challenges for IES scheduling. To deal these issues, the original robust scheduling model of IES is first reformulated as a mixed integer second-order cone programming (MISOCP) by convex relaxation method. Furthermore, to improve the economy and flexibility of robust scheduling solution, a light robust model is developed to address the multiple uncertainties from EHs. Finally, a consensus-based alternating direction method of multipliers (ADMM) approach is developed to tackle the robust scheduling model in MISOCP form, where EH operators parallelly solve their respective EH subproblems with limited information exchange, and subsequently the EH subproblem in MISOCP form is solved by a nonconvex ADMM (NC-ADMM) approach to guarantee the convergence of the consensus-based ADMM. Simulation results in a three-EH IES are presented to illustrate the effectiveness of proposed method for optimal synergy of multiple EHs with uncertainties.

A Multi-objective Optimal Dispatch Method for Integrated Energy System Considering Multiple Loads Variations

This paper proposes a multi-objective optimization dispatching method for the Integrated Energy System (IES), in which cooling/heating/electricity multi-load demand are included, and IES’s requirements in terms of energy efficiency, economy, and emissions are considered. We established an IES model for multi-objective optimal dispatch, and simulated cooling/heating/electric multiple load variations in it; then we established multiple objective functions based on IES’s scheduling requirements in terms of energy efficiency, economy, environmental issues. The model is solved by finding the pareto front based on genetic algorithm (GA), so that the decision makers can filter out the scheduling plan based on their preference, in which single-objective optimization is used first to find out the initial population for GA. Finally, the proposed method’s effectiveness is verified by case studies.

Optimal scheduling of regional integrated energy system considering integrated demand response

Flexible load can optimize the load curve, which is an important means to promote renewable energy consumption. The peculiarities of electricity, heat, cooling and gas loads are analyzed in this paper, considering the fuzzy degree of human perception of water temperature, the characteristic model of hot water load is established. Considering the fuzzy degree of human perception of ambient temperature, the characteristic model of cooling load is established by using PMV and PPD index. Meanwhile, considering four combinations of cut load, translatable load, transferable load and alternative load, and considering the coupling relationship of composite parts, different response models of load are established respectively. With the minimum cost of the system including operation and compensation costs as the objective function, the optimization scheduling model of regional integrated energy system is established, and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each equipment. The results show that the load curve can be optimized, the flexible regulation ability of the regional integrated energy system can be enhanced, the energy loss of the system can be reduced, and the wind power consumption ability of the system can be increased by considering the integrated demand response.

Optimal scheduling of regional integrated energy system considering multiple uncertainties

Integrated energy system (IES) is an effective way to realize the efficient utilization of energy. Under the deregulated electricity market, IES operator gains profits by providing customers with energy service, including electricity, heat or cooling energy. With the deepening of market reform, higher penetration rate of renewable energy, economic risks embed in the IES. Based on this, an optimal scheduling model of regional IES considering uncertainties is proposed, aiming at maximizing the profits. Scenario analysis method has been adopted to model the uncertainties: Markov-Chain-Monte-Carlo (MCMC) sampling method, which has a better performance in fitting the probability distribution, is utilized to generate scenarios; K-means clustering method is applied to narrow down the sampling sets. By replacing the parameters in the deterministic model with the sampling sets, a series of optimal results can be achieved. The case study shows that the cooling storage tank can improve the economic benefits about 4.97% by converting electricity to cooling energy at lower price period and releasing energy at peak hours. Besides, through the proposed optimization model, operators can have a straight understanding of the venture brought by the uncertainties and a more reliable scheduling result is formed for reference.

Research on complex integrated energy planning and operation optimization

The safe and stable operation of the energy system is the top priority of the world in the industrial field. The energy integrated system optimizes the system through multiple channels and cascade utilization of energy, which greatly improves the utilization rate of energy and provides a convenient way for the development of low-carbon society. In this paper, the composite integrated energy system model is constructed through the analysis of the structure of the integrated energy system. On this basis, a coordinated optimal scheduling method for integrated energy system with multiple energy stations is proposed, which takes the system operation economy as the goal. The case simulation results show that the coordinated operation of energy stations can greatly improve the stability and economy of the hybrid integrated energy system.

Optimization Strategy of Multiarea Interconnected Integrated Energy System Based on Consistency Theory

A collaborative optimization strategy of an integrated energy system aiming at improving energy efficiency is studied in this paper for the cluster optimization of an integrated energy system (IES). In this paper, an improved discrete consistency method based on the coordination optimization method for IES is proposed. An IES model considering the mixed energy supply of electricity, heat, and gas is constructed in a single region. And then an objective function with the maximum return is established, on the premise of assuming that the prices of electricity, heat, and gas can be used as an economical means to adjust the energy utilization. Finally, the consistency theory is applied to the IES, and the improved discrete consistency algorithm is utilized to optimize the objective function. In the case study, a certain region IES is taken as an example in Northeast China. The case study demonstrates the effectiveness and accuracy of the coordination optimization method for IES.

Weighted directed graph based matrix modeling of integrated energy systems

The integrated energy system (IES) has attracted great attention for its significant role in energy efficiency improvement, energy conservation and emission reduction, in which the modeling naturally becomes a hot topic in related fields. In this study, a matrix modeling method based on graph theory is proposed for the multi-energy flows of IES, where the energy converters and subsystems are abstracted into branches and nodes so as to construct a weighted directed graph model of IES with the establishment of an energy balance equation. Energy storage devices are also integrated into the proposed method by defining virtual storage nodes and charging/discharging branches, and the model of IES is established in a unified matrix form. In case studies, the superiorities of the proposed method in complexity, computational efficiency and flexibility are demonstrated in comparison with a number of state-of-the-art approaches. The computational burden of the operational optimization for the IES including energy storage devices is significantly reduced, and the economic benefits of various energy storage devices are evaluated.

An operating state estimation model for integrated energy systems based on distributed solution

In view of the disadvantages of the traditional energy supply systems, such as separate planning, separate design, independent operating mode, and the increasingly prominent nonlinear coupling between various sub-systems, the production, transmission, storage and consumption of multiple energy sources are coordinated and optimized by the integrated energy system, which improves energy and infrastructure utilization, promotes renewable energy consumption, and ensures reliability of energy supply. In this paper, the mathematical model of the electricity-gas interconnected integrated energy system and its state estimation method are studied. First, considering the nonlinearity between measurement equations and state variables, a performance simulation model is proposed. Then, the state consistency equations and constraints of the coupling nodes for multiple energy subsystems are established, and constraints are relaxed into the objective function to decouple the integrated energy system. Finally, a distributed state estimation framework is formed by combining the synchronous alternating direction multiplier method to achieve an efficient estimation of the state of the integrated energy system. A simulation model of an electricity-gas interconnected integrated energy system verifies the efficiency and accuracy of the state estimation method proposed in this paper. The results show that the average relative errors of voltage amplitude and node pressure estimated by the proposed distributed state estimation method are only 0.0132% and 0.0864%, much lower than the estimation error by using the Lagrangian relaxation method. Besides, compared with the centralized estimation method, the proposed distributed method saves 5.42 s of computation time. The proposed method is more accurate and efficient in energy allocation and utilization.

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.

Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output

The intermittency and uncertainty output of renewable energy source (RES) poses a challenge for the optimal dispatch of an integrated energy system (IES). Many current studies focus on the time-domain characteristics of the RES prediction, while ignoring the time-frequency characteristics of the prediction, thus the operation is usually scheduled on a uniform time-step. Based on this context, an adaptive hybrid dispatch time-step (AHDT) determination method based on the time-frequency characteristics of the prediction is proposed. Different from the traditional time-step, AHDT is non-uniform. Firstly, the Hilbert-Huang transform is used to convert the predicted RES output data to the time-frequency. Next, a time-step function which reflects the mapping of time-step and instantaneous frequency is constructed to suggest an adaptive time-step in each dispatch segment. Then, an optimal day-ahead dispatch model of a typical IES with AHDT, which is essentially a mixed integer quadratic programming (MIQP) problem, is established. Finally, the day-ahead schedules of each unit are obtained by solving the MIQP problem. The simulations are conducted in a total of twenty randomly selected days. The results show that the AHDT method is superior to the uniform time-step method for its effect in reducing the operating cost and the optimization-calculating time cost.

Stochastic optimization of integrated energy system considering network dynamic characteristics and psychological preference

To improve the flexibility of integrated energy system (IES) and promote the clean energy accommodation, an IES stochastic optimization model considering the network dynamic characteristics and psychological preference is proposed. Firstly, the uncertainty and spatial-temporal correlation between multiple wind farms are dealt by the auto-regressive and moving average (ARMA) time series and Cholesky decomposition, and then typical scenarios are constructed by the scenario method. Secondly, considering the dynamic characteristics of heat and gas networks such as heat network delay, thermal inertia of buildings and gas linepack, the IES model which coordinates the supply side, transmission side and demand side is proposed. Finally, based on the mean-variance Markowitz theory and endowment effect in behavioral psychology, the influence of human psychological preference (such as risk concern and loss aversion) on the IES is reflected. A 52-node electricity-gas-heat IES is used to verify the proposed model. The simulation results reveal that the network dynamic characteristics and demand response (DR) can effectively improve the IES flexibility and wind power accommodation. Meanwhile, the psychological preference and wind power spatial-temporal correlation also have significant influence on the IES optimization.

Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response

Multi energy demand response is an important measure to achieve the economic and efficient operation of the integrated energy system (IES), which is of great significance to promote the sustainable development of the IES. The concept of demand response (DR) is extended to IES, and a double objective operation optimization model of IES considering integrated demand response (IDR) mechanism is proposed. Firstly, based on the electro-thermal IES, this paper proposes a demand response mechanism considering electric load and thermal load. According to the load composition (transferable load, reducible load, adjustable thermal load), the load management strategy is established. Then, based on the DR mechanism of electro-thermal IES, a multi-objective operation optimization model is established for the first time, which takes the economic benefits and comprehensive energy efficiency as the objective function. The model involves the equipment constraint in the process of energy production and energy transmission. The relationship between total operation cost and comprehensive energy efficiency of electro-thermal IES is analyzed in the part of Data, Simulation Results and Analysis. Finally, the results show that the model effectively improves the economic benefits and comprehensive energy efficiency of IES, and reduces the pollutants emissions to a certain extent.

A data-driven distributionally robust operation model for urban integrated energy system

The multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system (UIES). Meanwhile, the uncertainties of renewable energy resources (e.g., wind energy) also bring great challenges to the operation of UIES. In this study, a typical two-stage data-driven distributionally robust operation (DDRO) model based on finite scenarios is proposed for UIES including power, gas and heat networks to obtain a salient strategy from both the economic and robustness perspectives. In the first stage, the forecasted information for wind power is especially included to improve the economic aspect of robust decisions. The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power difference caused by real-time wind power uncertainty is mitigated by flexible regulation of energy purchasing and coupling units (such as gas turbine, power to gas equipment, electric boiler and gas boiler). Moreover, norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of the uncertain wind power. The whole two-stage model is solved by the column-and-constraint generation (CCG) algorithm. Finally, Case studies are conducted to show the performance of the proposed model and approach.

Probability-Interval-Based Optimal Planning of Integrated Energy System With Uncertain Wind Power

Owing to a higher energy supply efficiency and operational flexibility, integrated energy system (IES), including the power, heating, and gas systems, will be the primary form of energy supply in the future. However, with the increase of large-scale stochastic wind power integration, the IES planning will face a significant challenge as the traditional power system. Therefore, a probability-interval-based IES planning considering wind power integration is proposed in this article. First, a conditional value-at-risk (CVaR) based probability-interval method is developed to describe the uncertain wind power. Second, beside traditional facilities, electricity storage system is introduced to improve the flexibility of IES. Then, an expansion planning model for IES is established to minimize the total cost including investment, operation, CVaR cost, and unserved energy cost. Moreover, the piecewise linearization method is used to deal with the nonlinear integral terms of the proposed model to improve the solution efficiency. Finally, IEEE14-NGS14 and IEEE118-NGS40 systems are constructed and the planning model is solved by GAMS/CPLEX. The numerical results illustrate the correctness and effectiveness of the proposed method.

Market clearing model of distribution network-side integrated energy system

In an electric-gas integrated energy system that considers the coupling conditions between systems, there is energy transaction between the systems. So the node energy price setting method and market clearing strategy of the coupled system are worth studying. First of all, this paper establishes a market clearing model for electricity / gas subsystems and a method for solving node energy prices. Then, a market clearing model of the electrical coupling system is established based on this. Finally, an example to verify the effectiveness of the nodal energy price formulation method and the market clearing model is given.

Optimal scheduling of building integrated energy system based on demand response

With the development of renewable energy technology, integrating a variety of renewable energy integrated energy systems can effectively solve the problem of optimizing the scheduling of buildings with high energy consumption and fast growth rate. Based on the modeling and analysis of various energy equipment in the system, the integrated energy system of building buildings, based on the demand response compensation price, with the lowest construction operating cost as the goal function, establishes the optimization scheduling model of building-level integrated energy system based on demand response, and uses the particle group algorithm based on cloud model improvement to optimize the solution of the model. The study is introduced for simulation to compare the two different modes of participation in demand response, and the optimal performance of cloud model particle group algorithm and elementary particle group algorithm. The results show that the cloud model particle group algorithm model based on demand response can effectively save the operating cost of the building-level integrated energy system, and reduce the power grid side load peak and valley difference.

A methodological approach to the development of a technological platform for intelligent integrated energy system modelling

Modeling of intelligent integrated energy systems, which has a complex structure, is performed using specialized software, which is caused by the computational complexity of the models, methods and algorithms used. The paper lists the requirements for software for modeling intelligent integrated energy systems and concludes that it is necessary to develop a technological platform. A methodological approach to the development of a technological platform for modeling integrated energy systems is presented. The methodological approach includes the following components: principles of building a technological platform, architecture of a technological platform, and the structure of an ontology system, a method of automated integration of software components into a software system.

Modeling of Integrated Energy System of Offshore Oil and Gas Platforms Considering Couplings Between Energy Supply System and Oil and Gas Production System

The integrated energy system (IES) of offshore oil and gas platforms is a complex energy intensive system, which is composed of energy supply system (ESS), oil and gas production system (OGPS), diesel oil supply system (DOSS) and oil storage and transportation system (OSTS). Among them, ESS and OGPS are closely coupled and show a trend on stronger coupling due to implementation of new technologies, such as waste heat recovery and associated gas utilization. In order to reduce the operating cost and carbon emissions, an optimal operation model that considers their couplings is proposed. Firstly, based on the standardized matrix modeling method, subsystems inside OGPS and ESS are modeled individually and then combined, through which the model of the whole IES can be built. Then, representing this model as constraints, the optimal operation model of offshore oil and gas platforms' IES is proposed, together with constraints representing operational limitations of subsystems. Particularly, in the proposed model, couplings on heat, associated gas and electrical energy between OGPS and ESS are modeled, and hybrid energy storage system (HESS) is considered in modeling ESS, which is able to coordinate associated gas storage (AGS) and electricity storage (ES) in the optimal operation.

Individual-based distributed parallel optimization for operation of integrated energy systems considering heterogeneous structure

This paper proposes an individual-based distributed parallel (IBDP) method for the operation optimization of integrated energy systems considering heterogeneous structure. The proposed IBDP method utilizes individual-based models (IBMs) to describe integrated energy systems. In this model, three basic modelling units are summarized, from which, a general three-level model of integrated energy systems is inherited. Utilizing IBMs, the high-dimensional integrated energy system model is decoupled into distributed heterogeneous individuals. The individuals are with local information inside to protect their information privacy and independent operation from the environment. Meanwhile, global information, which provides with environmental boundary conditions, can be obtained through individual interactions. To solve the IBMs of integrated energy systems efficiently, an Proximal Jacobian alternating direction multiplier algorithm (Proximal Jacobian ADMM) is adopted to implement the IBDP where the subproblems of individuals run in parallel. To verify the veracity and stability of the proposed method, the IBDP is applied to optimize the operation strategy of integrated energy systems in four different scales, and the results are compared with that obtained by centralized optimization method.

Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning

<p indent=0mm>The integrated energy systems are developing in the direction of ubiquitous power Internet of Things (IoT). The main feature is the large-scale integration of distributed energies, loads, and cogenerations, which usually brings random disturbances to the systems, thus causing frequency stability control problems, where cannot be effectively addressed by the traditional automatic generation control methods. The recently developed machine learning approach provides potential solutions for complex systems with random disturbances. However, when this approach is applied to the ultra-large-scale ubiquitous power IoT systems, the dimensionality related problem arises, and it should be solved. In this paper, a deep reinforcement learning algorithm is developed for the frequency stability control of the ultra-large-scale ubiquitous power IoT systems with random disturbances. The developed algorithm is based on the idea of a proportional priority sampling mechanism and the prioritized replay DDQN-AD (PRDDQN-AD) strategy. In this work, both the two-region integrated energy system model and the multi-regional ubiquitous power IoT integrated energy system model are adopted in simulation and analysis; these models include a large number of sources, loads, energy-storage units, and grids. Simulation and comparison results show that the training quality of samples, learning efficiency, and generalization performance of the strategy are improved by using PRDDQN-AD. The strategy has a fast convergence speed, and thus can successfully solve the dimensionality problem.