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Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

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APPLIED ENERGY 2022年 307卷 118170-118170页

作者： Ma, Mingtao Huang, Huijun Song, Xiaoling Pena-Mora, Feniosky Zhang, Zhe Chen, Jie Nanjing Univ Sci & Technol Dept Management Sci & Engn Nanjing 210094 Peoples R China Columbia Univ Adv Construct & Informat Technol Lab New York NY 10027 USA Columbia Univ Ctr Bldg Infrastruct & Publ Space New York NY 10027 USA

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs;(2) peak shaving;(3) frequency regulation;and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.

关键词： Shared energy storage bi-level optimization model Sizing and operations Renewable energy Interactive genetic algorithm Energy storage sharing

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A two-phase market clearing framework for inter-provincial electricity trading in Chinese power grids

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SUSTAINABLE CITIES AND SOCIETY 2022年 85卷

作者： Chen, Yi Wang, Han Yan, Zheng Xu, Xiaoyuan Zeng, Dan Ma, bin Shanghai Jiao Tong Univ Dept Elect Engn Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China China Elect Power Res Inst Nanjing 210037 Jiangsu Peoples R China State Grid Anhui Elect Power Co LTD Hefei 230022 Anhui Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

To fully utilize the energy resources, inter-provincial electricity trading have been conducted in multiple Chinese provincial power grids. This paper proposes a two-phase market clearing framework to facilitate inter-provincial electricity trading in Chinese power grids. In Phase one, the pre-processing of bid curves is implemented to address the multi-segment bid behaviors of massive market participants. As the pre-processing methods, network partition, curve aggregation and curve approximation are developed to reduce the computation complexity. In Phase two, a bi-level model is established to depict the market clearing process, which includes the upper-level model for inter-provincial trading and the lower-level model for intra-provincial trading. Those two models are iteratively solved by passing the optimal electricity purchase price and electricity demands to each other. Nu-merical experiments based on the IEEE systems and the practical Chinese inter-provincial power grids are carried out to verify the validity and scalability of the proposed framework. The simulation results indicate that: 1) the pre-processing of bid curves effectively reduce the computational burdens;2) the inter-provincial market pro-motes the utilization of energy resources and increases the total social welfare. Further, the errors caused by the pre-processing methods are analyzed and the availability of the proposed framework is discussed.

关键词： Inter -provincial electricity trading Market clearing Network partition bid curve aggregation bi-level optimization model

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Wind Versus Storage Allocation for Price Management in Wholesale Electricity Markets

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第2期11卷 817-827页

作者： Masoumzadeh, Amin Nekouei, Ehsan Alpcan, Tansu AGL Portfolio Planning & Optimizat Grp Melbourne Vic 3008 Australia KTH Royal Inst Technol Div Decis & Control S-11428 Stockholm Sweden City Univ Hong Kong Dept Elect Engn Kowloon Tong Hong Kong Peoples R China Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bi-level optimization model is developed, which computes the optimal allocation of new wind and battery capacities, by minimizing a weighted sum of the average market price and price volatility. A fixed budget is allocated on wind and battery capacities in the upper-level problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lower-level problem using a stochastic (Bayesian) Cournot-based game model. Australia's national electricity market, which is experiencing occasional price peaks, is considered as the case study. Our simulation results quantitatively illustrate that the regulated wind is more efficient than storage in reducing the average price, while the regulated storage more effectively reduces the price volatility. According to our numerical results, the storage-only solution reduces the average price at most by 9.4%, and the wind-only solution reduces the square root of price volatility at most by 39.3%. However, an optimal mixture of wind and storage can reduce the mean price by 17.6% and the square root of price volatility by 48.1%. It also increases the consumer surplus by 1.52%. Moreover, the optimal mixture of wind and storage is a profitable solution unlike the storage-only solution.

关键词： Electricity supply industry Generators Resource management Stochastic processes optimization Batteries Nanoelectromechanical systems Electricity market bi-level optimization model average price price volatility regulated wind-storage firm

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A New Commerce Operation model for Integrated Energy System Containing the Utilization of bio-Natural Gas

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ENERGIES 2020年第24期13卷 6607-6607页

作者： Zhang, Guoliang Lou, Suhua Wu, Yaowu Wu, Yang Wen, Xiangfeng Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol 1037 Luoyu Rd Wuhan 430074 Peoples R China Power Dispatch & Control Ctr China Southern Power Guangzhou 510623 Peoples R China

To promote the collaborative development of the bio-natural gas (BNG) industry and the integrated energy system (IES), this paper proposes a new commerce operation model considering the gas price adjustment mechanism for the IES with the utilization of bio-natural gas. The bi-level optimization model is used to simulate the clearing process within the open energy market framework, and the uncertainties of variable renewable energy output are modeled with a set of scenarios through the stochastic programming approach. In the upper-level model, the energy management center adjusts the bio-natural gas price rationally to minimize the expected total operating cost and release the price signal to the lower-level model;the lower-level model simulates the sub-markets clearing process to formulate detailed operation schemes. The bi-level model is transformed into a mathematical programming problem with equilibrium constraints (MPEC) through the Karush-Kuhn-Tucher (KKT) condition of the lower-level model, and the nonlinear model is converted into a mixed-integer linear programming problem and solved. The numerical results verified the effectiveness of the proposed model.

关键词： integrated energy system distributed renewable energy sources bio-natural gas open energy markets bi-level optimization model

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Optimal scheduling of resources for a price-maker distribution company in electricity markets considering network component failures

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 122卷 106200-106200页

作者： Moghimi, Farzad Hassanzadeh Barforoushi, Taghi Mousavi, Seyed Milad Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Sar Iran

Electricity generation and storage resources, demand-side participation, along with modern communication infrastructure, have led distribution companies (DisCos) to increasingly play a strategic role in wholesale electricity markets. Although these resources have increased the security of power supply in modern distribution networks, this is still affected by random failures of distribution and sub-transmission network components. Therefore, the main issue that needs to be addressed is how the random failure of the components of the network has an impact on the performance of the strategic DisCos. In this paper, a new framework is proposed to assist a strategic DisCo to determine the strategy of purchasing electricity from the wholesale market and selling electricity to end-users, considering the failure of network components. The proposed model is designed as a stochastic bi-level optimization problem that, at the upper-level, aims at maximizing the expected profit of the DisCo, optimal decisions on the use of resources, and prices offered to the wholesale and retail markets are adopted. Distributed generation (DG) units, battery-based storage sysetm, demand response (DR) programs, and load curtailment (LC) are the resources of the DisCo. Random failures of components are modeled by a set of scenarios. The expected energy not supplied (EENS) is used as the reliability index. The lower-level problem involves the wholesale market clearing problem, with the aim of maximizing the social welfare in which the power allocated to DisCo as well as locational marginal prices (LMPs) are calculated. The model is recast to the MPEC model and then linearized to a MILP model by KKT optimality conditions and dual theorem. Simulations show that, due to the constraints in the distribution network, the failure of distribution and sub-transmission lines reduces the strategic role of the DisCo in the wholesale market. The proposed model can used for analyzing the behavior of strategic

关键词： Battery-based storage system bi-level optimization model Component failure Real-time pricing (RTP) Strategic DisCo

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Interactive nonlinear multiobjective optimal design of water distribution systems using Pareto navigator technique

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SUSTAINABLE CITIES AND SOCIETY 2021年 73卷 103110-103110页

作者： Moazeni, Faegheh Khazaei, Javad Penn State Harrisburg Sch Sci Engn & Technol Civil & Environm Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Harrisburg Elect Engn Dept 777 W Harrisburg Pike Middletown PA 17057 USA Penn State Univ Pk Architectural Engn Dept State Coll PA 16801 USA

Sustainable water distribution systems comprise complex combinations of critical components that are simultaneously optimized. The trade-offs and interactions that exist among these components, however, will introduce difficult challenges to optimization algorithms. In this paper, a multiobjective optimization formulation is proposed to scrupulously manage the demand, leakage, and age of water, as well as energy consumption of water distribution systems. Pareto navigator approach, a learning-oriented algorithm, is used to attain the final Pareto optimal solution for water network. The optimal power consumption, leakage, demand, and water age are used as inputs to the upper level energy resource allocation to optimize the energy demand and operational cost of integrated water-energy system. Numerical results show 76.6%, 95.4%, 13.14%, and 27.4% reduction in leakage, water age, water usage, and energy consumption of water network over 24 h, respectively, while the daily cost of electricity generation reduces by 3.7%. The energy system is designed based on isolated energy units, also known as microgrids, with minimum dependency on the fossil-fuel-based power plants. As such, the proposed multiobjective bi-level optimization model will also improve the carbon footprint of water distribution systems and their impact on climate change.

关键词： Multiobjective optimization Pareto optimal solution Water-energy nexus bi-level optimization model Mixed integer nonlinear programming Sustainable systems

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A new two-layer model for energy management in the smart distribution network containing flexi-renewable virtual power plant

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 194卷 107085-107085页

作者： Asl, Saeed Abrisham Foroushan Bagherzadeh, Leila Pirouzi, Sasan Norouzi, Mohammadali Lehtonen, Matti Islamic Azad Univ Dept Elect Engn Saveh Branch Saveh Iran Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Islamic Azad Univ Semirom Branch Power Syst Grp Semirom Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Aalto Univ Dept Elect Engn & Automat Espoo Finland

This paper presents a two-layer energy management model (EMM) in the smart distribution network (SDN) considering flexi-renewable virtual power plants (FRVPPs) that participate in the day-ahead energy and reserve markets. The first layer of EMM is applied to the FRVPPs to maximize their profit in the proposed markets subjected to the constraints of renewable and flexible sources with considering coordination between these sources and VPP operator (VPPO). Also, the second layer of EMM creates coordination between VPPOs and the distribution system operator to manage the SDN based on minimizing the summation of network energy loss and voltage deviation function as a linear normalized objective function while it subjects to the linear format of AC optimal power flow equations. This model contains uncertainties of load, market price, maximum power of renewable energy sources and demand of flexible sources, where stochastic programming is used to model these uncertain parameters. The proposed model includes bi-level optimization model that is solved by the Benders decomposition approach to achieve an optimal solution at low calculation time. Finally, the capabilities of the proposed model have been investigated by implementing on the IEEE 69-bus distribution network.

关键词： Benders decomposition method bi-level optimization model Day-ahead energy and reserve market Flexi-renewable virtual power plant Smart distribution network Two-layer energy management model

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Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems

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APPLIED ENERGY 2021年 281卷 116142-116142页

作者： Song, Xiaoling Wang, Yudong Zhang, Zhe Shen, Charles Pena-Mora, Feniosky Nanjing Univ Sci & Technol Dept Management Sci & Engn Nanjing 210094 Peoples R China Columbia Univ Adv Construct & Informat Technol Lab New York NY 10027 USA Columbia Univ Ctr Bldg Infrastruct & Publ Space New York NY 10027 USA

Even though economic dispatch of integrated electricity and natural gas systems using optimization techniques has been widely conducted, a cooperative distributed and multi-objective optimization is still needed for meeting multi-energy demands from both conventional and renewable energy resources. In this study, an original bi-level economic-environmental equilibrium model is proposed to optimize a dispatch strategy for an integrated system, which include combined cooling, heating, and power and power to gas technologies. By optimizing cooperative distributed decisions, the system attempts to provide heat/cooling, power and natural gas loads. An equilibrium consideration of economic energy supply and environmental carbon emission control is achieved using a multi-objective optimization in the model. To reflect interactions of individual system operators in a cooperative distributed manner, a bi-level optimization is applied to address the respective decisions and objective functions. Subsequently, an interactive solution approach based on Non-dominated Sorting Genetic Algorithm-II and genetic algorithm is specifically designed to solve the model by updating individual decisions interactively and iteratively, and the optimality of an integrated solution lies in the capability to coordinate two system operators for a reliable, economic and environmental energy supply. Performance of the dispatch strategy is validated for a case with various system configurations. The obtained results showed that multi-energy needs are met, economic-environmental trade-offs achieved and hierarchical interactions addressed. Proper managerial suggestions are also provided based on comparison analyses. The proposed method is therefore worthy of being popularized in integrated systems.

关键词： bi-level optimization model Economic-environmental equilibrium Multi-objective optimization Integrated electricity and natural gas system Interactive solution approach Multi-energy demand

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Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 130卷 103309-103309页

作者： Ma, Chengyuan Yu, Chunhui Yang, Xiaoguang Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai Peoples R China

Trajectory planning for connected and automated vehicles (CAVs) has the potential to improve operational efficiency and vehicle fuel economy in traffic systems. Despite abundant studies in this research area, most of them only consider trajectory planning in the longitudinal dimension or assume the fully CAV environment. This study proposes an approach to the decentralized planning of CAV trajectories at an isolated signalized intersection under the mixed traffic environment, which consists of connected and human-driven vehicles (CHVs) and CAVs. A bi-level optimization model is formulated based on discrete time to optimize both the longitudinal and lateral trajectories of a single CAV given signal timings and the trajectory information of surrounding vehicles. The upper-level model optimizes lateral lane-changing strategies. The lowerlevel model optimizes longitudinal acceleration profiles based on the lane-changing strategies from the upper-level model. Minimization of vehicle delay, fuel consumption, and lane-changing costs are considered in the objective functions. A Lane-Changing Strategy Tree (LCST) and a Parallel Monte-Carlo Tree Search (PMCTS) algorithm are designed to solve the bi-level optimization model. CAV trajectories are planned one by one according to their distance to the stop bar. A rolling horizon scheme is applied for the dynamic implementation of the proposed model with time-varying traffic conditions. Numerical studies validate the advantages of the proposed trajectory planning model compared with the benchmark cases without CAV trajectory planning. The average fuel consumption and lane-changing numbers of CAVs can be reduced noticeably, especially with high traffic demand. The delay of CAVs is reduced by similar to 2 s on average, which is limited due to the fixed signal timing plans. The trajectory planning of CAVs also reduces the delay and the fuel consumption of CHVs and the mixed traffic, especially with high penetration rates of CAVs. The

关键词： Mixed traffic environment Trajectory planning Longitudinal and lateral trajectory bi-level optimization model

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optimization of Multi-energy Storage Configurations for Regional Integrated Energy Systems 4th

Optimization of Multi-energy Storage Configurations for Regi...

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4th International Forum on Smart Grid Protection and Control (Purple Mountain Forum)

作者： Xia, Xuan Tao, Ran Zhao, Dongmei North China Elect Power Univ Beijing 102206 Peoples R China

ISBN: (纸本)9789811397837;9789811397820

The configurations of multi-energy storage devices in the regional integrated energy system (RIES) can greatly improve the economic benefits of the system and it is an important research direction of RIES planning. However, at present the research on the optimization of electric/thermal/gas multi-energy storage configuration in RIES is insufficient. Under this background, the method to deploy the rated capacity and power of multi-energy storage systems for RIES containing power to gas (P2G) device was proposed in this paper. bi-level optimization model was established in the purpose of considering the interaction between the configuration and operation of the multi-energy storage systems at the same time. The upper level was optimized for configuration, and the lower level was optimized for operation. By the Kuhn-Tucker approach, the bi-level problem was transformed into a single level optimization problem that can be solved with Gurobi optimizer. Finally, the correctness of the proposed model was verified by an example.

关键词： Regional integrated energy system Multi-energy storage systems bi-level optimization model Configurations

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