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energy management of Price-maker Community energy Storage by Stochastic Dynamic Programming

CSEE Journal of Power and energy Systems 2024年第2期10卷 492-503页

作者： Lirong Deng Xuan Zhang Tianshu Yang Hongbin Sun Yang Fu Qinglai Guo Shmuel S.Oren Department of Electrical Engineering Shanghai University of Electric PowerShanghai 200000China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen 518055China Department of Industrial Engineering and Operations Research University of California BerkeleyBerkeleyCAUSA Risk Analytics and Optimization Chair EPFLSwitzerlandand also with Power Systems LaboratoryETH Zurich8092 ZurichSwitzerland State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.

关键词： Analytical stochastic dynamic programming energy management energy storage price-maker social welfare

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energy management Optimization Based on Aging Adaptive Functional State Model of Battery for Internal Combustion Engine Vehicles

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Automotive Innovation 2023年第1期6卷 62-75页

作者： Weiwei Kong Tianmao Cai Yugong Luo Xiaomin Lian Fachao Jiang College of Engineering China Agricultural UniversityBeijing 100083China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.

关键词： Internal combustion engine vehicles Aging adaptive functional state model energy management

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Improved energy management Strategy for Prosumer Buildings with Renewable energy Sources and Battery energy Storage Systems

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN energy 2024年第2期12卷 381-392页

作者： Sharma, Pavitra Saini, Krishna Kumar Mathur, Hitesh Datt Mishra, Puneet Birla Inst Technol & Sci Dept Elect & Elect Engn Pilani India

The concept of utilizing microgrids (MGs) to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits. These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems (BESSs) to deal with the uncertain nature of renewable energy sources. However, because of the high capital investment of BESS and the limitation of available energy, there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS. In this regard, this paper proposes an improved energy management strategy (IEMS) for the prosumer building to minimize the operating cost of MG and degradation factor of BESS. Moreover, to estimate the practical operating life span of BESS, this paper utilizes a non-linear battery degradation model. In addition, a flexible load shifting (FLS) scheme is also developed and integrated into the proposed strategy to further improve its performance. The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic (PV) and BESS-powered AC-DC hybrid MG installed at a commercial building. Moreover, the scenario reduction technique is used to handle the uncertainty associated with generation and load demand. To validate the performance of the proposed (s)trategy, the results of IEMS are compared with the well-established energy management strategies. The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS. Moreover, FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS, thus making the operation of prosumer building more economical and efficient.

关键词： Prosumer building battery energy storage system (BESS) battery degradation factor demand response energy management microgrid solar photovoltaic (PV) system DEGRADATION COST MODEL

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Real-Time energy management for Net-Zero Power Systems Based on Shared energy Storage

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN energy 2024年第2期12卷 371-380页

作者： Du, Pengbo Huang, Bonan Liu, Ziming Yang, Chao Sun, Qiuye Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China State Grid Liaoning Elect Power Co Ltd Informat & Commun Branch Shenyang 110004 Peoples R China

Battery energy storage systems (BESSs) serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems. However, the efficiency and cost performance have remained significant challenges, which hinders the widespread adoption and development of BESSs. To address these challenges, this paper proposes a real-time energy management scheme that considers the involvement of prosumers to support net-zero power systems. The scheme is based on two shared energy storage models, referred to as energy storage sale model and power line lease model. The energy storage sale model balances real-time power deviations by energy interaction with the goal of minimizing system costs while generating revenue for shared energy storage providers (ESPs). Additionally, power line lease model supports peer-to-peer (P2P) power trading among prosumers through the power lines laid by ESPs to connect each prosumer. This model allows ESP to earn profits from the use of power lines while balancing power deviations and better consuming renewable energy. Experimental results validate the effectiveness of the proposed scheme, ensuring stable power supply for net-zero power systems and providing benefits for both the ESP and prosumers.

关键词： energy storage Real-time systems Net zero energy management Costs Power system stability Power generation Shared energy storage energy storage provider (ESP) energy management peer-to-peer (P2P) trading net-zero power system SHARING FRAMEWORK OPTIMIZATION STRATEGY PV

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Risk-constrained energy management Strategy for a Commercial Campus Considering Comprehensive Reserves Against Islanding Conditions

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CSEE Journal of Power and energy Systems 2023年第5期9卷 1797-1807页

作者： Zheming Liang Desong Bian Xiaohu Zhang Di Shi GEIRI North America San JoseCAUSA.Z.M.Liang is also with Huaneng Renewables Co.Ltd.Beijing 100036China

This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding capabilities.The goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management strategy.To face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is leveraged.Extensive simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.

关键词： Comprehensive comfort levels CVaR energy management islanding capability reserve stochastic programming uncertainty

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Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

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Digital Communications and Networks 2023年第3期9卷 797-808页

作者： Pengfei Du Hongjiang Lei Imran Shafique Ansari Jianbo Du Xiaoli Chu The Engineering Research Center of Intelligent Air-ground Integrated Vehicle and Traffic Control Ministry of EducationXihua UniversityChengdu610039China Chongqing Key Lab of Mobile Communications Technology Chongqing University of Posts and TelecommunicationsChongqing400065China James Watt School of Engineering University of GlasgowGlasgowG128QQUK Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and TelecommunicationsXi'an710121China Electronic and Electrical Engineering University of SheffieldSheffieldS102TNUK

energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

关键词： Cellular networks energy harvesting energy management Chance-constrained Distributionally robust optimization

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energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultra-capacitor hybrid energy storage system

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第8期14卷 535-553页

作者： Jun-yi LIANG Jian-long ZHANG Xi ZHANG Shi-fei YUAN Cheng-liang YIN National Engineering Laboratory for Automotive Electronic Control Technology Shanghai Jiao Tong University

To solve the low power density issue of hybrid electric vehicular batteries, a combination of batteries and ultracapacitors (UCs) could be a solution. The high power density feature of UCs can improve the performance of battery/UC hybrid energy storage systems (HESSs). This paper presents a parallel hybrid electric vehicle (HEV) equipped with an internal combus- tion engine and an HESS. An advanced energy management strategy (EMS), mainly based on fuzzy logic, is proposed to improve the fuel economy of the HEV and the endurance of the HESS. The EMS is capable of determining the ideal distribution of output power among the internal combustion engine, battery, and UC according to the propelling power or regenerative braking power of the vehicle. To validate the effectiveness of the EMS, numerical simulation and experimental validations are carried out. The results indicate that EMS can effectively control the power sources to work within their respective efficient areas. The battery load can be mitigated and prolonged battery life can be expected. The electrical energy consumption in the HESS is reduced by 3.91% compared with that in the battery only system. Fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle.

关键词： energy management Fuel economy Parallel hybrid electric vehicle Hybrid energy storage system (HESS) Fuzzy logic

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Multi-energy management of Interconnected Multi-microgrid System Using Multi-agent Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean energy 2023年第5期11卷 1606-1617页

作者： Sichen Li Di Cao Weihao Hu Qi Huang Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Southwest University of Science and Technology MianyangChina Department of energy Technology Aalborg UniversityDK-9220 AalborgDenmark

The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.

关键词： Interconnected multi-microgrid system energy management combined heat and power demand response deep reinforcement learning

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Half‑Power Prediction and Its Application on the energy management Strategy for Fuel Cell City Bus

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Automotive Innovation 2023年第1期6卷 131-142页

作者： Longhai Zhang Lina Ning Xueqing Yang Sheng Zeng Tian Yuan Gaopeng Li Changchun Ke Junliang Zhang Institute of Fuel Cells School of Mechanical EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China Yutong Bus Co LtdZhengzhou 450016China

The fuel cell hybrid powertrain is a potential power supply system for fuel cell vehicles.The underlying problem is that the fuel cell vehicles encounter exhaustive hydrogen consumption.To effectively manage hydrogen consumption,the aim is to propose fuel cell city bus power and control system.The underlying idea is to determine the target power of fuel cell through simulation study on fuel cell and battery energy management strategy and road test verifications.A half-power prediction energy management strategy is implemented to predict the target power of the fuel cell in the current time step based on the demand power of the vehicle and the state of charge(SOC)of the battery in the previous time steps.This offers better understanding of the correlation between fuel cell power and vehicle drive cycle for enabling effective power supply management.The research results show that the half-power prediction energy management strategy effectively reduces the hydrogen consumption of the vehicle by 7.1%and the number of battery cycle by 6.0%,compared to the stepped manage-ment strategy of battery SOC.When applied to a 12-m fuel cell city bus—F12,specially designed and manufactured for the Winter Olympic Games in 2022—the fuel economy of 3.7 kg/100 km is achieved in urban road conditions.This study lays a foundation for providing the powertrain configuration and energy management strategy of fuel cell city bus.

关键词： Fuel cell city bus energy management Half-power prediction

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Microgrid energy management with energy Storage Systems:A Review

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CSEE Journal of Power and energy Systems 2023年第2期9卷 483-504页

作者： Xiong Liu Tianyang Zhao Hui Deng Peng Wang Jizhen Liu Frede Blaabjerg energy Electricity Research Center Jinan UniversityZhuhai 519070China School of Electrical and Electronic Engineering Nanyang Technological University639798 Singapore School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of energy Technology at Aalborg University Denmark

Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy management.However,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased operation.This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques.First,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management.Second,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management.Mathematical programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation schemes.Finally,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.

关键词： Architecture energy management energy storage systems microgrids optimization uncertainty models

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