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Optimal operation of virtual power plants with shared energy storage

IET SMART GRID 2023年第2期6卷 147-157页

作者： Chen, Wenxule Xiang, Yue Liu, Junyong Sichuan Univ Coll Elect Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

The emergence of the shared energy storage mode provides a solution for promoting renewable energy utilization. However, how establishing a multi-agent optimal operation model in dealing with benefit distribution under the shared energy storage is still a challenge. Considering the multi-agent integrated virtual power plant (VPP) taking part in the electricity market, an energy trading model based on the sharing mechanism is proposed to explore the effect of the shared energy storage on multiple virtual power plants (MVPPs). To analyse the relationship among MVPPs in the shared energy storage system (SESS), a game-theoretic method is introduced to simulate the bidding behaviour of VPP. Furthermore, the benefit distribution problem of the virtual power plant operator (VPPO) is formulated based on the Nash bargaining theory. In the case study, the proposed method is conducted in four VPPs with different resource endowments in terms of techno-economic and operation efficiency. Results verify that the multiple virtual power plants with a shared energy storage system interconnection system based on the sharing mechanism not only can achieve a win-win situation between the VPPO and the SESS on an operation cost but also obtain the optimal allocation scheme and improves the operation efficiency of the VPPs.

关键词： Optimisation techniques Power system management, operation and economics Knowledge based systems renewable energy sources multi-agent systems power markets operation efficiency energy storage energy management systems multiagent integrated virtual power plant shared energy storage mode Game theory power generation economics game theory multiagent optimal operation model virtual power plant operator shared energy storage system interconnection system renewable energy utilization energy trading model Power engineering computing power engineering computing virtual power plants Distributed power generation multiple virtual power plants stochastic programming sharing mechanism

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stochastic Unit Commitment: Model Reduction via Learning

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Current Sustainable/Renewable Energy Reports 2023年第2期10卷 36-44页

作者： Liu, Xuan Conejo, Antonio J. Constante Flores, Gonzalo E. Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Department of Integrated Systems Engineering The Ohio State University ColumbusOH43210 United States Davidson School of Chemical Engineering Purdue University West LafayetteIN47907 United States

Purpose of Review: As weather-dependent renewable generation increases its share in the generation mix of most electric energy systems, a stochastic unit commitment becomes the natural day-ahead scheduling tool. However, such a tool is generally computationally intractable if a detailed uncertainty description is considered. Taking this into account, we proposed a learning method to make the stochastic unit commitment problem tractable. Recent Findings: Recent advances in statistical learning and machine learning to address optimization problems can be advantageously applied to the rather intractable stochastic unit commitment problem. Considering these advances, we explore simple learning techniques to drastically reduce the size of a stochastic unit commitment problem without significantly altering its optimal solution. Summary: The considered stochastic unit commitment problem is formulated as a two-stage stochastic programming problem. The first stage represents commitment decisions, while the second one represents the operation conditions under different scenarios. Taking into account historical solved instances (or proxies for them), we reduce the size (measured by numbers of constraints and variables) of the stochastic unit commitment problem by (i) fixing unchanged binary variables and by (ii) eliminating inactive inequality constraints. Our numerical results show that the reduced problem generally requires significantly less time to solve while obtaining high-quality solutions, which are very close to or indistinguishable from the one obtained by solving the original problem. We use an Illinois 200-bus system to illustrate and characterize the performance of the proposed problem-reduction method. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： stochastic programming

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Non-parametric generalised newsvendor model

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ANNALS OF OPERATIONS RESEARCH 2023年第1-2期321卷 241-266页

作者： Ghosh, Soham Mukhoti, Sujay Indian Inst Technol Indore Humanities & Social Sci Indore 453552 Madhya Pradesh India Indian Inst Management Indore Operat Management & Quantitat Tech Area Indore 453556 Madhya Pradesh India

In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries.

关键词： stochastic programming Non-parametric estimation Strong consistency Monte-Carlo simulation Newsvendor problem Non-linear optimisation

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stochastic Microgrid Sizing Considering Electric Vehicle Charging Demand

Stochastic Microgrid Sizing Considering Electric Vehicle Cha...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Magalhaes, Marcelo Montandon Terada, Lucas Zenichi Cortez, Juan Carlos Soares, Joao Rider, Marcos J. Vale, Zita Univ Estadual Campinas Dept Energy Syst DSE Campinas Brazil Polytech Porto GECAD Sch Engn ISEP Porto Portugal

ISBN: (纸本)9798350386509;9798350386493

Microgrids represent a pivotal shift in energy technology, offering a suite of advantages over conventional power grids, such as improved reliability, cost efficiency, and environmental benefits due to their ability to integrate renewable sources and provide flexible energy solutions, underscoring the urgent need for grid modernization and strategic planning. This paper presents a stochastic economic model of linear programming (LP) for microgrid sizing, considering the charging scheduling profiles of electric vehicles (EVs) within a grid. The model is based on linear approximation techniques for the operation of EV and battery energy storage system (BESS). The model also incorporates the injection of energy from EV into energy distributions system (EDS) using vehicle-to-grid (V2G) technology, which considers compensation for the owners.

关键词： Distributed energy resources Electric vehicles Microgrids sizing stochastic programming Vehicle-to-grid

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A two-stage stochastic optimization model for port infrastructure planning

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MARITIME ECONOMICS & LOGISTICS 2024年第2期26卷 185-211页

作者： Bhurtyal, Sanjeev Hernandez, Sarah Eksioglu, Sandra Yves, Manzi Univ Arkansas 4190 Bell Engn Ctr Fayetteville AR 72701 USA Univ Arkansas Dept Ind Engn 4190 Bell Engn Ctr Fayetteville AR 72701 USA

This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.

关键词： Inland waterway port Benders decomposition algorithm stochastic programming Port infrastructure investment

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Optimal power bidding of overseas PV plants in Singapore wholesale electricity market

ENERGY CONVERSION AND ECONOMICS

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ENERGY CONVERSION AND ECONOMICS 2024年第2期5卷 73-77页

作者： Xu, Yan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Singapore's power sector has set targets of net-zero emissions by 2050. Given the limited land space of the country, a key strategy to decarbonize the power grid is to import clean power from renewable energy resources such as photovoltaic (PV) plants installed at overseas locations. The present electricity market rules require such overseas PV plants to maintain constant power generation during each bidding period. To meet such requirements, energy storage systems (ESSs) are to be deployed in the PV plants to compensate for the PV power fluctuation. This paper proposes an optimal power bidding approach for maximizing the profit of the PV plant participating in the Singapore wholesale electricity market. The problem is formulated as a stochastic programming model, which takes the short-term PV power forecasting as the input, maximizes the expected profit considering the PV power selling revenue and the penalty cost for power shortfall during each bidding cycle (30 min), and satisfies constraints of the ESS. The proposed method can also be used for determining the optimal size of the ESS. Simulation results have verified the effectiveness of the proposed method.

关键词： electricity market energy storage system optimal power bidding photovoltaic stochastic programming

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A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

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Data Science 2024年第1期7卷 13-27页

作者： Henao, César Augusto Porto, Andrés Felipe González, Virginia I. Department of Industrial Engineering Universidad Del Norte Barranquilla Colombia Department of Industrial Engineering Institución Universitaria Americana Barranquilla Colombia Departament d'Organització d'Empreses Universitat Politècnica de Catalunya Barcelona Spain

In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand. © 2024 - The authors. Published by IOS Press.

关键词： Multiskilling personnel scheduling retail stochastic programming workforce flexibility

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Battery Bidding Strategy Under Uncertainty Considering Market Practical Situations

Battery Bidding Strategy Under Uncertainty Considering Marke...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Ren, Weihang Guan, Yongpei Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

ISBN: (纸本)9798350381849;9798350381832

Storing off-peak electricity and supplying it during peak demand offers advantages in energy production, social impact, and environmental preservation. At the same time, private battery owners can profit from this energy arbitrage, driving diverse storage methods. Yet, varying market conditions and nonconvex nature of the electricity pricing lead to unpredictable market schemes. This paper employs stochastic programming to develop robust bidding strategies for uncertain market situations. We create efficient algorithms for battery owners, generate bidding strategies in each market, and analyze their structural performance. Numerical experiments demonstrate our algorithms' empirical performance, offering insights for battery owners.

关键词： Battery bidding energy storage stochastic programming

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Multi-stage Dynamic Scheduling Optimization for Autonomous Mobility-on-Demand Systems 21

Multi-stage Dynamic Scheduling Optimization for Autonomous M...

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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Tian, Hangqi Wang, Ning Guo, Yuntao Tongji Univ Sch Automot Studies 4800 Caoan Rd Shanghai Peoples R China Tongji Univ Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Autonomous Mobility-on-Demand (AMoD) system is booming in urban transportation, however, the uncertain and complex fleet dynamic scheduling remains one of the main challenges for operators. Therefore, this study proposes a multistage dynamic scheduling approach for shared autonomous electric vehicles (SAEVs). Specifically, considering the matching characteristics of the different tasks, the vehicle order-taking and charging tasks are modelled as bipartite graph maximum weight matching problems with the objectives of maximizing order revenue and minimizing charging mileage. The vehicle repositioning task is modelled as integer programming with the objective of minimizing the vehicle dispatch mileage, and stochastic chance-constrained programming is applied to cope with uncertain user demands. A solution strategy combining the KM algorithm with Gurobi solver and greedy strategy is designed to obtain the real-time scheduling instructions. Finally, numerical experiments based on real travel data validate the advantages of the proposed approach in reducing fleet scheduling costs and improving system operational revenues.

关键词： Shared autonomous electric vehicles Dynamic scheduling stochastic programming Kuhn-Munkres algorithm

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Risk Quantification with Management of Aggregated Energy Resources

Risk Quantification with Management of Aggregated Energy Res...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Roy, Trishant Hedman, Kory W. Arizona State Univ Sch Elect Comp & Energy Engn ECEE Tempe AZ 85281 USA

ISBN: (纸本)9798350381849;9798350381832

Unpredictabilities and uncertainties are inherent in the management of distributed energy resources (DERs). This poses operational challenges for the utility/aggregator managing the resources and brings the need for an efficient decision-making framework under uncertainty for participation in electricity markets. This paper presents mixed-integer linear programming formulations considering the aggregator's risk preferences/tolerance in the day-ahead and real-time markets. Since DERs also introduce risks for the system operator responsible for the reliability of the electric grid, effective and easy-to-interpret risk quantification methodologies are expected to be beneficial from a practical outlook. A near-term risk metric is developed from the operator's perspective to assess the future performance of aggregators to help them make appropriate decisions. Various simulations using the NREL PERFORM dataset are conducted to analyze the effect of different risk preferences of the aggregator on both the profitability and the quantified risk metric.

关键词： Distributed energy resources aggregation uncertainty assessment electricity markets stochastic programming

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