A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Optimal Multi-Timescale Scheduling of Integrated Energy Systems with Hybrid Energy Storage System Based on Lyapunov Optimization

The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

Dispatch of a Coal Mine-Integrated Energy System:Optimization Model with Interval Variables and Lower Carbon Emission

In the coal mining process,a large amount of Coal Mine-Associated energy(CMAE),such as coal mine methane and underground wastewater,is produced.Research on the modeling and optimization dispatching of a Coal Mine-Integrated Energy System(CMIES)with CMAE effectively saves energy and reduces carbon pollution.CMAE has great uncertainties owing to the affections of the hydrogeology conditions and mining schedules.In addition,thermal loads have high comfort requirements in mines,which brings great challenges to the optimization dispatching of CMIESs.Therefore,this paper studies the architecture and solution of CMIESs with a flexible thermal load and source-load uncertainty.First,to effectively improve the electric and thermal conversion efficiency,the architecture of CMIES,including a concentrating solar power station,is built.Second,for the scheduling model with bilateral uncertainty,the interval representation method with interval variables is proposed,and a multi-objective scheduling model based on the interval variables and flexible thermal load is constructed.Finally,we propose a solution method for the model with interval variables.A case study is conducted to demonstrate the performance of our model and method for lowering carbon emissions and cost.

Two-stage Multi-objective Optimization and Decision-making Method for Integrated Energy System Under Wind Generation Disturbances

Although integrated energy systems(IES)are currently modest in size,their scheduling faces strong challenges,stemming from both wind generation disturbances and the system’s complexity,including intrinsic heterogeneity and pronounced non-linearity.For this reason,a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration(MOGSOPE)is proposed to efficiently achieve the optimal solution under wind generation disturbances.The optimizer has an embedded trainable surrogate model,Deep Neural Networks(DNNs),to explore the common features of the multiscenario search space in advance,guiding the population toward a more efficient search in each scenario.Furthermore,a multiscenario Multi-Attribute Decision Making(MADM)approach is proposed to make the final decision from all alternatives in different wind scenarios.It reflects not only the decisionmaker’s(DM)interests in other indicators of IES but also their risk preference for wind generation disturbances.A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms.With respect to numerical performance metrics HV,IGD,and SI,the proposed optimizer exhibits improvements of 3.1036%,4.8740%,and 4.2443%over MOGSO,and 4.2435%,6.2479%,and 52.9230%over NSGAII,respectively.What’s more,the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated,particularly in optimal scheduling of IES under wind generation disturbances.

Integrated operational planning of hydrothermal power and natural gas systems with large scale storages

The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.

Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage

A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.

Towards Fully Renewable Energy Systems:Experience and Trends in Denmark

Deployment of renewable energy generation capacities and integration of their power production into existing power systems has become a global trend,with a common set of operational challenges stemming from variability and limited predictability of power generation from,e.g.,wind and solar.Denmark is a country that invested early in wind energy,rapidly proposing very ambitious goals for the future of its energy system and global energy usage.While the case of Denmark is specific due to its limited size and good interconnections,there may still be a lot to learn from the way operational practice has evolved,also from shifting towards a liberalized electricity market environment,and more generally from going along with other technological and societal evolution.The aim of this paper is to give an overview of recent and current initiatives in Denmark that contributes towards a goal of reaching a fully renewable energy system.

Evaluating the Vulnerability of Integrated Electricity-heat-gas Systems Based on the High-dimensional Random Matrix Theory

Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.

Coordination of battery energy storage and power-to-gas in distribution systems

Concerning the rapid development and deployment of Renewable Energy Systems(RES)and Energy Storage System(ESS)including Power-to-Gas(PtG)technology can significantly improve the friendliness of the integration of renewable energy.The purpose of this paper is to develop a coordination strategy between a battery energy storage and a PtG system.A simulation case is created with an electrical and a natural gas grid as well as steady-state models of RES and PtG.Charging strategies are developed accordingly for the ESS as well as production strategies for the PtG system.The size of the ESS is then observed with regards to the RES and PtG systems.As a result,it is found that surplus energy from RES can be stored and then used to support the electrical grid and the natural gas grid.It is also concluded that the capacity of the ESS can be affected,given a proper charging and production strategy,which needs to be tailored to each system.As shown in the paper,due to an improper charging strategy in the first quarter of a month,the ESSPC size has increased from its optimal size of 314 MWh to roughly 576 MWh.It can also be seen that given a proper charging strategy,this capacity can be less than 200 MWh.

From demand response to integrated demand response: review and prospect of research and application

In the traditional power system demand response, customers respond to electricity price or incentive and change their original power consumption pattern accordingly to gain additional benefits. With the development of multi-energy systems (MES) in which electricity, heat, natural gas and other forms of energy are coupled with each other, all types of energy customers are able to participate in demand response, leading to the concept of integrated demand response (IDR). In IDR, energy consumers can response not only by reducing energy consumption or opting for off-peak energy consumption but also by changing the type of the consumed energy. Taking the traditional demand response in power system as a starting point, the studies of the fundamental theory, framework design and potential estimation of demand response in power system are reviewed, and the practical cases and software development of demand response are introduced. Finally, the current theoretical research and application of IDR are assessed.

Relaxed Alternating Direction Method of Multipliers for Hedging Communication Packet Loss in Integrated Electrical and Heating System

Integrated electrical and heating systems(IEHSs)are promising for increasing the flexibility of power systems by exploiting the heat energy storage of pipelines.With the recent development of advanced communication technology,distributed optimization is employed in the coordination of IEHSs to meet the practical requirement of information privacy between different system operators.Existing studies on distributed optimization algorithms for IEHSs have seldom addressed packet loss during the process of information exchange.In this paper,a distributed paradigm is proposed for coordinating the operation of an IEHS considering communication packet loss.The relaxed alternating direction method of multipliers(R-ADMM)is derived by applying Peaceman-Rachford splitting to the Lagrangian dual of the primal problem.The proposed method is tested using several test systems in a lossy communication and transmission environment.Simulation results indicate the effectiveness and robustness of the proposed R-ADMM algorithm.

Active-reactive power scheduling of integrated electricity-gas network with multi-microgrids

Advances in natural gas-fired technologies have deepened the coupling between electricity and gas networks,promoting the development of the integrated electricity-gas network(IEGN)and strengthening the interaction between the active-reactive power flow in the power distribution network(PDN)and the natural gas flow in the gas distribution network(GDN).This paper proposes a day-ahead active-reactive power scheduling model for the IEGN with multi-microgrids(MMGs)to minimize the total operating cost.Through the tight coupling relationship between the subsystems of the IEGN,the potentialities of the IEGN with MMGs toward multi-energy cooperative interaction is optimized.Important component models are elaborated in the PDN,GDN,and coupled MMGs.Besides,motivated by the non-negligible impact of the reactive power,optimal inverter dispatch(OID)is considered to optimize the active and reactive power capabilities of the inverters of distributed generators.Further,a second-order cone(SOC)relaxation technology is utilized to transform the proposed active-reactive power scheduling model into a convex optimization problem that the commercial solver can directly solve.A test system consisting of an IEEE-33 test system and a 7-node natural gas network is adopted to verify the effectiveness of the proposed scheduling method.The results show that the proposed scheduling method can effectively reduce the power losses of the PDN in the IEGN by 9.86%,increase the flexibility of the joint operation of the subsystems of the IEGN,reduce the total operation costs by $32.20,and effectively enhance the operation economy of the IEGN.

Market-oriented Optimal Dispatching Strategy for a Wind Farm with a Multiple Stage Hybrid Energy Storage System

With the increased promotion of integrated energy power systems(IEPS),renewable energy and energy storage systems(ESS)play a more important role.However,the fluctuation and intermittent nature of wind not only results in substantial reliability and stability defects,but it also weakens the competitiveness of wind generation in the electric power market.Meanwhile,the way to further enhance the system reliability effectively improving market profits of wind farms is one of the most important aspects of Wind-ESS joint operational design.In this paper,a market-oriented optimized dispatching strategy for a wind farm with a multiple stage hybrid ESS is proposed.The first stage ESS is designed to improve the profits of wind generation through day-ahead market operations,the real-time marketbased second stage ESS is focused on day-ahead forecasting error elimination and wind power fluctuation smoothing,while the backup stage ESS is associated with them to provide the ancillary service.An interval forecasting method is adopted to help to ensure reliable forecast results of day-ahead wind power,electricity prices and loads.With this hybrid ESS design,supply reliability and market profits are simultaneously achieved for wind farms.

Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.

Optimal Location Allocation Strategy of Gas-fired Unit in Transmission Network

The gas-fired generation has recently become an important power source for power systems.The increasing integration of gas-fired units(GFUs)brings a problem of location allocation strategy for power system planners.This paper proposes a bi-level maximum-minimum optimal placement model of GFUs to improve the static voltage stability in the transmission network.In the first stage,the locations of installed GFUs are optimized to improve the static voltage stability margin.The optimal installed capacity of GFUs is determined to minimize the operation costs and power losses in the second stage.The proposed mixed-integer nonlinear programming(MINLP)model is solved by second-order cone programming relaxations.Numerical results in the IEEE 118-bus test system demonstrate the effectiveness of the proposed method and the static voltage stability can be improved.