Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Acquah, Moses Amoasi; Kodaira, Daisuke; Han, Sekyung

doi:10.3390/en11051166

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…To prevent the utility power from exceeding the contracted power in real-time scheduling, a surcharging system is considered in the second scheduling stage. The electric meter installed by Korea Electric Power Corporation (KEPCO), South Korea, estimates the peak power over a 15-min period [34]. When the peak power that updated from the electric meter exceeds the contracted power, KEPCO imposes extra charges for overuse.…”

Section: Inequality Constraintsmentioning

confidence: 99%

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

et al. 2020

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A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers.Commercial optimization solvers generally have superior computational capability compared with their open-source counterparts. The ED implementation environment is then programmed either in general-purpose programming languages (GPLs), e.g., Python and Julia, or in algebraic modeling languages (AMLs). AML is a high-level programming language dedicated to optimization applications. Popular AMLs include GAMS and AMPL.Numerous studies on ED have been conducted from the perspectives of its optimization algorithms [4][5][6][7][8][9][10] and actual implementation methods [11][12][13][14][15]. Table 1 summarizes the optimization algorithms, optimal solvers, programming languages, and demonstration methods used in these studies. The optimization algorithms to the ED problem have been well studied. Particularly, in references [4-10], various analytic-based optimization methods, such as linear programming (LP), mixed-integer programming (MIP), and convex optimization, have been investigated for the application to ED. In reference [4], day-ahead ED was formulated as an LP problem to determine the hourly planned operation set points (i.e., charging and discharging power references) of battery-based energy storage systems (ESSs) and electric vehicles (EVs). In reference [5], convex optimization method was used to determine the optimal charging and discharging schedules of an ESS. In reference [6], an online battery power control method based on an MIP formulation was used over a rolling horizon window. The ED strategies proposed in [4][5][6] planned the operation schedule only once and the schedule is followed without any modifications during operation. However, one-time scheduling strategies are vulne...

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Section: Inequality Constraintsmentioning

confidence: 99%

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

et al. 2020

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“…Since electric demand exists in real-life with uncertainties a DMS solution that ignores the uncertainty factor is susceptible to errors. In such a case the BESS schedule will not be robust, and peak demand might not be resolved in cases where demand forecast substantially deviates from observed demand [13]. Figure 1a shows demand forecast as a probability distribution in the day horizon, this is denoted as a demand probability distribution (DPD) profile.…”

Section: Dynamic-interval Density Forecastmentioning

confidence: 99%

“…The study does not consider peak demand control using BESS. The research by [13] discusses a real-time dynamic interval density forecast to account for future demand uncertainties. The DSM solution is focused on the use of stationary BESS for peak demand control using stochastic optimization.…”

Section: Introductionmentioning

confidence: 99%

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Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties

Acquah

Han

2019

Energies

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Robust operation of load management control for a building is important to account for the uncertainty in demand as well as any distributed sources connected to the building. This paper discussed an online load management control solution using distributed energy storage (DES) while considering uncertainties in demand as well as DES to reduce peak demand for economic benefit. In recent years’ demand-side management (DSM) solutions using DES such as stationary energy management system (BESS) and plugged-in electric vehicles (PEV) have been popularised. Most of these solutions resort to deterministic load forecast for the day ahead energy scheduling and do not consider the uncertainties in demand and DES making these solutions vulnerable to uncertainties. This study presents an online density demand forecast, k-means clustering of PEV groups and stochastic optimisation for robust operation of BESS and PEV for a building. The proposed method accounts for uncertainties in demand and uncertainties due to mobile energy storage as presented by PEVs. For a case study, we used data obtained from an industrial site in South Korea. The verified results as compared to other methods with a deterministic approach prove the solution is efficient and robust.

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“…A reliability study of a power system with DSM is presented in [1]. Since a plan for load curtailment can only be prepared when load forecasting is reliable, stochastic optimization and Gray Wolf Optimization (GWO)-based load forecasting and subsequent DSM programs are presented in [2,3]. Load shifting is also an alternative method of performing DSM.…”

mentioning

confidence: 99%

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Singh

Jha

2019

Energies

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This paper proposes Object-Oriented Usability Indices (OOUI) for multi-objective Demand Side Management (DSM). These indices quantify the achievements of multi-objective DSM in a power network. DSM can be considered as a method adopted by utilities to shed some load during peak load hours. Usually, there are service contracts, and the curtailments or dimming of load are automatically done by service providers based on contract provisions. This paper formulates three indices, namely peak power shaving, renewable energy integration, and an overall usability index. The first two indices indicate the amount of peak load shaving and integration of renewable energy, while the third one combines the impact of both indices and quantifies the overall benefit achieved through DSM. The application of the proposed indices is presented through simulation performed in a grid-tied microgrid environment for a multi-objective DSM formulation. The adopted microgrid structure consists of three units of diesel generators and two renewable energy sources. Simulation has been done using MATLAB software. Teaching-Learning-Based Optimization (TLBO) is adopted as the optimization tool due to its simplicity and independency of algorithm-specific control parameters. Five different cases of renewable energy availability with results validate the efficiency of the proposed approach. The results indicate the usefulness in determining the suitable condition regarding DSM application.Energies 2019, 12, 370 2 of 25 problems is the main area of research for system operators. DSM has appeared as a potential solution for many of these problems.Any scheme that involves alteration in operation of a complex system should be reliable to a certain level. A reliability study of a power system with DSM is presented in [1]. Since a plan for load curtailment can only be prepared when load forecasting is reliable, stochastic optimization and Gray Wolf Optimization (GWO)-based load forecasting and subsequent DSM programs are presented in [2,3]. Load shifting is also an alternative method of performing DSM. In this aspect, a corrective load shifting program is found to be a better option to conventional preventive load shifting [4,5]. In another work, loads are categorized in rigid and flexible types, and DSM is performed keeping in mind the adaptability of the flexible loads [6]. A novel application of DSM to smoothing the peaks of load curve in a small demand area in the Sultanate of Oman is described in [7]. The key contribution was load profile prediction based upon which the DSM program was prepared. The DSM program developed here handles the issue of variable power generation from solar energy-based generation. In a work, the uncertainty due to wind power generation and its impact on the DSM scheme has been discussed [8]. The methodology discusses the fulfillment of two key objectives of DSM, i.e., minimization of emission and cost. Battery storage and diesel-based energy sources are also employed with solar or wind generators to improve the reliability ...

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Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Cited by 14 publications

References 24 publications

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System

Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

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