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Smart Ev Charging in Pv-Powered Workplace Parking Lots: A Case Study from Denmark

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SSRN

SSRN 2024年

作者： Secchi, Mattia Rivera, Carlos Hermana Zepter, Jan Martin Marinelli, Mattia Technical University of Denmark Department of Wind and Energy Systems Kgs. Lyngby2800 Denmark

The increased uptake of electric vehicles (EVs) requires public charging for EV owners without access to home chargers. We propose an easy-to-implement centralised unidirectional (V1G) smart charging algorithm for parking lots and compare it firstly against uncoordinated charging, then against decentralised SC approaches. Six scenarios are described, either minimising the CO2 emissions or the EV charging costs, eventually including a PV system. For an exemplary workplace parking lot in Denmark, the cost reduction is limited to 3\% by the pricing scheme, while emissions-based smart charging achieves 10\% CO2 emissions and a 2.9% charging costs reductions. The peak demand is also shaved without affecting the SC efficiency, while making optimal use of an existing rooftop PV system, with 13% costs and 11% emissions reductions respectively. Additionally, an algorithm is proposed to distribute the SC benefits between the EV owners based on the experienced delay, ensuring none incur a loss. © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Scenario-Based Robust Optimization of Tree Structures

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arXiv 2024年

作者： Angelopoulos, Spyros Dürr, Christoph Elenter, Alex Melidi, Georgii Sorbonne Université CNRS LIP6 Paris France

We initiate the study of tree structures in the context of scenario-based robust optimization. Specifically, we study Binary Search Trees (BSTs) and Huffman coding, two fundamental techniques for efficiently managing and encoding data based on a known set of frequencies of keys. Given k different scenarios, each defined by a distinct frequency distribution over the keys, our objective is to compute a single tree of best-possible performance, relative to any scenario. We consider, as performance metrics, the competitive ratio, which compares multiplicatively the cost of the solution to the tree of least cost among all scenarios, as well as the regret, which induces a similar, but additive comparison. For BSTs, we show that the problem is NP-hard across both metrics. We also show how to obtain a tree of competitive ratio ⌈log2(k + 1)⌉, and we prove that this ratio is optimal. For Huffman Trees, we show that the problem is, likewise, NP-hard across both metrics;we also give an algorithm of regret ⌈log2 k⌉, which we show is near-optimal, by proving a lower bound of ⌊log2 k⌋. Last, we give a polynomial-time algorithm for computing Pareto-optimal BSTs with respect to their regret, assuming scenarios defined by uniform distributions over the keys. This setting captures, in particular, the first study of fairness in the context of data structures. We provide an experimental evaluation of all algorithms. To this end, we also provide mixed integer linear program formulation for computing optimal trees. © 2024, CC BY.

关键词： mixed-integer linear programming

A Carryover Storage Valuation Framework for Medium-Term Cascaded Hydropower Planning: A Portland General Electric System Study

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arXiv 2024年

作者： Chen, Xianbang Liu, Yikui Zhong, Zhiming Fan, Neng Zhao, Zhechong Wu, Lei ECE Department Stevens Institute of Technology HobokenNJ07030 United States Electrical Engineering Department Sichuan University Chengdu610017 China Portland General Electric PortlandOR97204 United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ85721 United States

Medium-term planning of cascaded hydropower (CHP) determines appropriate carryover storage levels in reservoirs to optimize the usage of available water resources. This optimization seeks to maximize the hydropower generated in the current period (i.e., immediate benefit) plus the potential hydropower generation in the future period (i.e., future value). Thus, in the medium-term CHP planning, properly quantifying the future value deposited in carryover storage is essential to achieve a balanced trade-off between immediate benefit and future value. To this end, this paper presents a framework to quantify the future value of carryover storage, which consists of three major steps: i) constructing a model to calculate the maximum possible hydropower generation that a given level of carryover storage can deliver in the future period;ii) extracting the implicit locational marginal water value (LMWV) of carryover storage for each reservoir by applying a partition-then-extract algorithm to the constructed model;and iii) developing a set of analytical rules based on the extracted LMWV to effectively calculate the future value. These rules can be seamlessly integrated into medium-term CHP planning models as tractable mixed-integer linear constraints to quantify the future value properly, and can be easily visualized to offer valuable insights for CHP operators. Finally, numerical results on a CHP system of Portland General Electric demonstrate the effectiveness of the presented framework in determining proper carryover storage values to facilitate medium-term CHP planning. © 2024, CC BY.

关键词： mixed-integer linear programming

Offering strategy of a price-maker virtual power plant in the day- ahead market

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2023年第4期12卷 666-+页

作者： Nguyen-Hong, Nhung Quang, Khai Bui Thanh, Long Phan Vo Huynh, Duc Bui Hanoi Univ Sci & Technol Sch Elect & Elect Engn Dept Elect Engn Hanoi Vietnam

With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs' disadvantages. When the VPP's capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP's profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a mixed integer linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS' rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants' production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS' rated capacity of 100MWh, the ESS' rated discharging/charging power increased from 10MW to 50MW will increase VPP's profit from 45987$ to 49464$. The obtained res

关键词： Day-ahead market mathematical problem with equilibrium constraints mixed-integer linear programming price-maker renewable energy virtual power plants

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2023年 11卷

作者： Sanati, Hemen Moslehi, Ghasem Reisi-Nafchi, Mohammad Isfahan Univ Technol Dept Ind & Syst Engn Esfahan *** Iran

Given that about half of the produced energy in the world is consumed in industries, there has been an increasing con-cern about optimizing energy consumption in manufacturing sectors. As one of the most effective ways, proper produc-tion scheduling to reduce energy consumption is of crucial importance among researchers and manufacturers. This pa-per addresses an unrelated parallel machine energy-efficient scheduling problem with sequence-dependent setup times by considering different energy consumption tariffs. The setup times are studied in two modes: disjointed from/jointed to processing time. For each one of these problems, two mixed -integer linear programming models have been formulated. The presented models for the problem with setup time disjointed from processing time can solve up to 16 machines and 45 jobs. In contrast, this capability is changed to 20 machines and 40 jobs for processing time jointed to the setup time problem. Furthermore, a fix and relax heuristic algorithm is presented for large-size instances, which can solve instances of up to 20 machines and 100 jobs for each of the two considered prob-lems. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Association of European Operational Research Societies (EURO). This is an open access article under the CC BY-NC-ND license

关键词： Energy-efficient scheduling mixed-integer linear programming Fix and relax Sequence-dependent setup time Tariffs

Variable aggregation for nonlinear optimization problems

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arXiv 2025年

作者： Naik, Sakshi Biegler, Lorenz Bent, Russell Parker, Robert Department of Chemical Engineering Carnegie Mellon University PittsburghPA United States Applied Math and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM United States

Variable aggregation has been largely studied as an important presolve algorithm for optimization of linear and mixed-integer programs. Although some nonlinear solvers and algebraic modeling languages implement variable aggregation as a pre-solve reduction, the impact it can have on constrained nonlinear programs is unexplored. In this work, we formalize variable aggregation as a pre-solve algorithm to develop reduced-space formulations of nonlinear programs. A novel approximate maximum variable aggregation strategy is developed to aggregate as many variables as possible. Furthermore, aggregation strategies that preserve the problem structure are compared against approximate maximum aggregation. Our results show that variable aggregation can generally help to improve the convergence reliability of nonlinear programs. It can also help in reducing total solve time. However, Hessian evaluation can become a bottleneck if aggregation significantly increases the number of variables appearing nonlinearly in many constraints. © 2025, CC BY.

关键词： mixed-integer linear programming

Integrated Optimization and Game Theory Framework for Fair Cost Allocation in Community Microgrids

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arXiv 2025年

作者： Babu, K. Victor Sam Moses Chakraborty, Pratyush Pal, Mayukha ABB Ability Innovation Center Hyderabad500084 India Department of Electrical and Electronics Engineering BITS Pilani Hyderabad Campus Hyderabad500078 India

Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and reduced participant satisfaction. This paper presents a novel framework integrating multi-objective optimization with cooperative game theory for fair and efficient microgrid operation and cost allocation. The proposed approach combines mixed-integer linear programming for optimal resource dispatch with Shapley value analysis for equitable benefit distribution, ensuring both system efficiency and participant satisfaction. The framework was validated using real-world data across six distinct operational scenarios, demonstrating significant improvements in both technical and economic performance. Results show peak demand reductions ranging from 7.8% to 62.6%, solar utilization rates reaching 114.8% through effective storage integration, and cooperative gains of up to $1,801.01 per day. The Shapley value-based allocation achieved balanced benefit-cost distributions, with net positions ranging from -16.0% to +14.2% across different load categories, ensuring sustainable participant cooperation. © 2025, CC BY-NC-ND.

关键词： mixed-integer linear programming

Sample-Based Piecewise linear Power Flow Approximations Using Second-Order Sensitivities

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arXiv 2025年

作者： Buason, Paprapee Misra, Sidhant Molzahn, Daniel K. Los Alamos National Laboratory Los AlamosNM United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

The inherent nonlinearity of the power flow equations poses significant challenges in accurately modeling power systems, particularly when employing linearized approximations. Although power flow linearizations provide computational efficiency, they can fail to fully capture nonlinear behavior across diverse operating conditions. To improve approximation accuracy, we propose conservative piecewise linear approximations (CPLA) of the power flow equations, which are designed to consistently over- or under-estimate the quantity of interest, ensuring conservative behavior in optimization. The flexibility provided by piecewise linear functions can yield improved accuracy relative to standard linear approximations. However, applying CPLA across all dimensions of the power flow equations could introduce significant computational complexity, especially for large-scale optimization problems. In this paper, we propose a strategy that selectively targets dimensions exhibiting significant nonlinearities. Using a second-order sensitivity analysis, we identify the directions where the power flow equations exhibit the most significant curvature and tailor the CPLAs to improve accuracy in these specific directions. This approach reduces the computational burden while maintaining high accuracy, making it particularly well-suited for mixed-integer programming problems involving the power flow equations. © 2025, CC BY-NC-ND.

关键词： mixed-integer linear programming

Optimal User and Target Scheduling, User-Target Pairing, and Low-Resolution Phase-Only Beamforming for ISAC Systems

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arXiv 2025年

作者： Abanto-Leon, Luis F. Maghsudi, Setareh Ruhr University Bochum Germany

We investigate the joint user and target scheduling, user-target pairing, and low-resolution phase-only beamforming design for integrated sensing and commmunications (ISAC). Scheduling determines which users and targets are served, while pairing specifies which users and targets are grouped into pairs. Additionally, the beamformers are designed using few-bit constant-modulus phase shifts. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP) and challenging to solve. To address it, we propose an exact mixed-integer linear program (MILP) reformulation, which leads to a globally optimal solution. Our results demonstrate the superiority of an optimal joint design compared to heuristic stage-wise approaches, which are highly sensitive to scenario characteristics. © 2025, CC BY.

关键词： mixed-integer linear programming