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Stochastic Pinch Analysis to address multi-objective resources conservation problems with parametric uncertainties

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年 162卷 30-48页

作者： Arya, Deepika Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 Maharashtra India

An emphasis on sustainable growth helps decrease the initial capital and reduce the impact of industry on an ecologically sensitive environment through resource conservation. Pinch Analysis serves as a valuable tool to identify the potential for resource conservation, waste minimization, and resource costs. However, there are inherent uncertainties associated with these resource conservation networks. The concept of Pinch Analysis is broadened to multi-objective resource conservation problems incorporating parametric uncertainties in this paper. The objective functions are combined to form a single compound objective function using the weighted sum approach. For the compound objective function, prioritized sequences (or resource combinations) are generated through multi-objective prioritized costs to accommodate parametric uncertainties. The multi-objective prioritized cost vs. weights curve helps determine the unique prioritized sequences for different range of weights to achieve optimal solutions. An exact analytical expression for the maximum number of prioritized sequences is also derived. Furthermore, a method is presented to construct the complete Pareto-optimal front to represent all optimal solutions graphically. Two illustrative examples, the hydrogen conservation problem with two objectives (cost and emission) and the water conservation problem with three objectives (cost, land requirement, and emission), demonstrate the proposed method's utility and efficacy.(c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Parametric uncertainties Resource conservation Pinch analysis chance-constrained programming Pareto-optimal front

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Optimizing the resource cost in multiple resources allocation problem with parametric uncertainties

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2022年 178卷 25-37页

作者： Arya, Deepika Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Bombay 400076 Maharashtra India

A B S T R A C T Process industries often utilize a large number of valuable resources. To minimize operating costs and to promote sustainability, it is essential to prevent the overuse of resources. In conjunction with the prioritized cost concept, Pinch Analysis helps in achieving cost optimal multi-resource allocation networks. Previous works were restricted to optimizing resource allocation networks with deterministic parameters without considering uncertainties associated with them. Uncertainties in source and resource parameters are incorporated in this paper through chance-constrained programming and solved through Pinch-based and numerical optimization-based methods. The notion of prioritized cost is extended to include uncertainties in various network parameters. Based on specified reliability for the multi-resource allocation network, the proposed approach determines the optimal operating cost of the network, and the results are verified through Monte Carlo simulations. Two examples from the field of water conservation and material conservation are solved to establish the utility of the proposed method. Optimal combinations of resources are pictorially represented at different cost ratios through a novel sensitivity diagram.(c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Parametric uncertainties Resource conservation Resource allocation network chance-constrained programming Pinch Analysis Nonlinear optimization

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Optimal Defense Strategy Against Load Redistribution Attacks under Attacker's Resource Uncertainty: A Trilevel Optimization Approach

Optimal Defense Strategy Against Load Redistribution Attacks...

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IEEE PES Grid Edge Technologies Conference and Exposition (Grid Edge)

作者： Su, Jinshun Xie, Chengzhi Dehghanian, Payman Mehrani, Saharnaz George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Florida Atlantic Univ Dept Informat Technol & Operat Management Boca Raton FL 33431 USA

ISBN: (纸本)9781665460125

The wide deployment of advanced computer technologies and evolving digitalization in power systems monitoring and control will inevitably make the power grid more vulnerable to cyber adversaries. Regarded as a viable cyber attack mechanism against power grids, load redistribution (LR) attack may mislead the power re-dispatch and cause unnecessary load outages. In this research, we develop a strategy for optimal allocation of limited defensive resources to safeguard power systems against LR attacks. The proposed defense scheme against LR attack is formulated as a trilevel optimization problem. To capture the uncertainty of attacking resources, we present a chanceconstrained programming formulation where chance constraint is used to capture the possible variations in the attacker's actions constrained by the uncertain available resources. The KarushKuhn-Tucker (KKT) condition and Benders decomposition algorithm are applied to solve the trilevel optimization problem. Case studies on the IEEE 57-bus test system demonstrate the efficiency of the resulting defense decisions against LR attacks.

关键词： Load redistribution (LR) attack cyber attack trilevel optimization chance-constrained programming Benders decomposition

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Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi-Objective Nonlinear programming Model

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IRRIGATION AND DRAINAGE 2021年第1期70卷 103-116页

作者： Yan, Zehao Li, Mo Li, Zhong McMaster Univ Dept Civil Engn Hamilton ON Canada Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Effect Utilizat Agr Water Resources Minist Agr Harbin Heilongjiang Peoples R China

Water scarcity causes conflicts between natural resources and socio-economic development which reinforces the need for optimal allocation of irrigation water resources. Irrigation water resource allocation is a complex problem due to various uncertainties in natural conditions. In this study, a stochastic multi-objective nonlinear programming model is developed for irrigation water allocation under uncertainty. The model is capable of balancing the conflicting objectives of maximizing both net economic benefit (NEB) and irrigation water use efficiency (IWUE). Moreover, it can reflect the random nature of water availability, and provide alternative water allocation schemes in response to climate change. The applicability of the developed model is demonstrated by a case study in north-west China. Trade-offs between NEB and IWUE are presented. Irrigation water allocation schemes to cope with changing environments, including climate change and varying water availability, are also proposed. The results demonstrate that the developed model can generate solutions that save irrigation water while ensuring NEB. This model is a useful tool to support the formulation of optimized water resources management policies in a changing environment. (c) 2020 John Wiley & Sons, Ltd.

关键词： chance-constrained programming changing environment efficiency genetic algorithm irrigation water nonlinear optimization model

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A reliability-aware chance-constrained battery sizing method for island microgrid

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ENERGY 2022年第0期251卷 1页

作者： Huo, Da Santos, Marcos Sarantakos, Ilias Resch, Markus Wade, Neal Greenwood, David Cran field Univ Sch Water Energy & Environm Cran field MK43 0AL England Newcastle Univ Sch Engn Newcastle Upon Tyne NE1 7RU Northumberland England Wirtschaftsagentur Burgenland Forsch und Innovat G Europastrasse 1 A-7540 Gussing Austria

Island Microgrids can coordinate local energy resources, provide post-fault reliability improvements for local customers, and aggregate local power and energy resources to offer services to the wider system. A crucial component of an Island Microgrid is the battery energy storage system, which can manage local imbalances, alleviate constraints, and improve reliability by enabling post-fault islanding. A planning and sizing method is required to quantify and maximize the benefits of battery energy storage while avoiding over-investment and under-utilization. This paper combines comprehensive reliability assessment with chance-constrained convex optimization, via second-order cone programming, to optimally size energy storage within an Island Microgrid. chance constraints are applied to the battery state-of-charge to avoid sizing the energy storage to accommodate extreme cases of uncertainty, avoiding uneconomic investment. The probability of reaching a state-of-charge constraint also indicates the likelihood that the battery energy storage system will be unable to facilitate island operation in the event of an outage, which affects the Island Microgrid reliability. The method is demonstrated on a real Austrian distribution network as part of the MERLON project. Results illustrate that an optimal trade-off can be identified between system reliability and operating cost when the probability of violating the chance constraints is 4.8%.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Battery sizing BESS chance-constrained programming Distribution system Island microgrid Reliability Second-order cone programming

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Scheduling of Energy Hub Resources Using Robust chance-constrained Optimization

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IEEE ACCESS 2022年 10卷 129738-129753页

作者： Esmaeel Nezhad, Ali Nardelli, Pedro H. J. Sahoo, Subham Ghanavati, Farideh LUT Univ Sch Energy Syst Dept Elect Engn Lappeenranta 53850 Finland Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Univ Aveiro Dept Ind Engn & Management P-3810193 Aveiro Portugal

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory (IGDT) method.

关键词： chance-constrained programming energy hub robust optimization loadability index mixed-integer linear programming

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Uncertainties in the resource conservation problems: a review

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2022年第9期24卷 2681-2699页

作者： Arya, Deepika Bandyopadhyay, Santanu Indian Inst Technol Dept Energy Sci & Engn Mumbai 400076 Maharashtra India

Process integration, which started its development in the early 1970s, is an emerging branch of study for conserving various resources. Process integration studies the interdependencies among various process units at the system level and the development and use of tools for holistically designing process networks with generic optimization of resources and sustainable development. The problems addressed in process integration are often referred to as resource conservation or source-sink allocation problems. Most of these problems are solved with precise input parameters. However, due to a wide range of known and unknown factors, these input parameters are uncertain in practical applications. To make the designed network more reliable, these uncertainties should be incorporated at the targeting stage of the problem. Over the years, researchers have used various approaches for managing resource conservation networks under uncertainty. This review examines the different mathematical optimization approaches adopted for handling uncertainties associated with the resource conservation networks along with their practical applications in recent years. The paper primarily examines the four most common approaches used to address uncertainties in process integration: sensitivity analysis, chance-constrained programming, fuzzy optimization, and interval programming. Recent advances in handling uncertainties within the framework of process integration, covering both mathematical programming and Pinch analysis, are also discussed. The review ends with a discussion on the significance and contributions of recent approaches. Some of the important future research directions are also identified to be addressed using process integration and Pinch analysis. [GRAPHICS] .

关键词： Process integration Sensitivity analysis chance-constrained programming Fuzzy optimization Interval programming Pinch analysis

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Distributed optimal scheduling for coupled transmission-distribution integrated electricity-gas system

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期206卷 107759-107759页

作者： Lan, Peng Wu, Gang Shen, Xiaodong Lv, Lin Liu, Junyong Zhao, Houxiang Jawad, Shafqat Liu, Jinyuan Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China State Grid Sichuan Econ Res Inst Chengdu 610041 Peoples R China Power China Sichuan Elect Power Engn CO LTD Chengdu 610041 Peoples R China

The coordinated optimal scheduling of coupled transmission-distribution integrated electricity-gas system (CTD-IEGS) is of great significance to ensure the economy and security of the system operation. In this paper, an optimal scheduling model of CTD-IEGS is proposed. To deal with the influence of uncertainty on both trans-mission and distribution sides, a probabilistic reserve allocation model based on chance-constrained program-ming (CCP) method is proposed, and the discrete sequence transformation (DST) method is used to transform the CCP reserve constraints into an easy solvable deterministic form. Due to the interdependence between the transmission system operator (TSO) and distribution system operator (DSO), a distributed solution algorithm based on alternating direction method of multipliers (ADMM) is adopted to solve the optimal scheduling model of CTD-IEGS. Case studies show that the proposed distributed solution strategy can effectively realize the distributed scheduling and coordinated optimization of the transmission network and distribution network, and protect the information privacy of TSO and DSO. In addition, the CCP method can adjust the confidence level of reserve to meet the tolerance of transmission and distribution network decision-makers for different risks, and balance the operation economy and reliability of the system.

关键词： coupled transmission-distribution integrated electricity-gas system reserve allocation chance-constrained programming discrete sequence transformation distributed optimization

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Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem

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ENERGIES 2022年第13期15卷 4578页

作者： Alshammari, Motaeb Eid Ramli, Makbul A. M. Mehedi, Ibrahim M. King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21589 Saudi Arabia

A chance-constrained programming-based optimization model for the dynamic economic emission dispatch problem (DEED), consisting of both thermal units and wind turbines, is developed. In the proposed model, the probability of scheduled wind power (WP) is included in the set of problem-decision variables and it is determined based on the system spinning reserve and the system load at each hour of the horizon time. This new strategy avoids, on the one hand, the risk of insufficient WP at high system load demand and low spinning reserve and, on the other hand, the failure of the opportunity to properly exploit the WP at low power demand and high spinning reserve. The objective functions of the problem, which are the total production cost and emissions, are minimized using a new hybrid chaotic maps-based artificial bee colony (HCABC) under several operational constraints, such as generation capacity, system loss, ramp rate limits, and spinning reserve constraints. The effectiveness and feasibility of the suggested framework are validated on the 10-unit and 40-unit systems. Moreover, to test the robustness of the suggested HCABC algorithm, a comparative study is performed with various existing techniques.

关键词： power dispatch spinning reserve chance-constrained programming chaotic maps optimization

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Dynamic Operation Strategy of Battery Energy Storage System Considering Energy Arbitrage and Frequency regulation 5

Dynamic Operation Strategy of Battery Energy Storage System ...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Shengqi Wang, Yilin Shanghai Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781665490542

With the rapid development of battery energy storage systems (BESS) in power system, the multifunctional application of BESS in the energy arbitrage and frequency regulation is regarded as an effective way in improving the economic benefits. However, current operation strategy for BESS cannot fully considered the time-varying demand of energy arbitrage and the randomness of frequency regulation. Thus, this paper proposes a dynamic operation strategy (DOS) by considering the randomness of frequency regulation, and the results of the DOS consist of two core parameters as arbitrage power and droop coefficient. At last, the promising results shown that the BESS DOS can adjust the dispatch priority of BESS resource according to demand of the energy arbitrage, and enable BESS continuously track the frequency deviation signals.

关键词： battery energy storage system energy arbitrage frequency regulation dynamic programming chance-constrained programming

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