Illustrating the Benefits of Openness: A Large-Scale Spatial Economic Dispatch Model Using the Julia Language

Weibezahn, Jens; Kendziorski, Mario

doi:10.3390/en12061153

Cited by 17 publications

(11 citation statements)

References 24 publications

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“…The reactive power compensation equipment is configured at nodes to provide enough reactive power for balance. The proposed multi-stage transmission network planning model considering transmission congestion is a mixed integer linear programming model, which can be effectively solved through the commercial solver CPLEX [51,52] in MATLAB. Table 1.…”

Section: Case Studiesmentioning

confidence: 99%

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

et al. 2020

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The uncertainty of generation and load increases in the transmission network in the power market. Considering the transmission congestion risk caused by various uncertainties of the transmission network, the optimal operation strategies of the transmission network under various operational scenarios are decided, aiming for the maximum of social benefit for the evaluation of the degree of scenario congestion. Then, a screening method for major congestion scenario is proposed based on the shadow price theory. With the goal of maximizing the difference between the social benefits and the investment and maintenance costs of transmission lines under major congestion scenarios, a multi-stage transmission network planning model based on major congestion scenarios is proposed to determine the configuration of transmission lines in each planning stage. In this paper, the multi-stage transmission network planning is a mixed integer linear programming problem. The DC power flow model and the commercial optimization software CPLEX are applied to solve the problem to obtain the planning scheme. The improved six-node Garver power system and the simplified 25-node power system of Zhejiang Province, China are used to verify the effectiveness of the proposed multi-stage planning model.

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Section: Case Studiesmentioning

confidence: 99%

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

et al. 2020

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“…In this case, a campus MG scheduling was on time, and the daily operational costs were reduced by 58% using open-source optimization. In reference [19], a completely open-source-based ED environment was established using the open-source programming language Julia and its corresponding open-source optimization solver ECOS; computational capability was verified over a 1-year period for a massive operation dataset measured from a German transmission network. The results demonstrated that the open-source environment is suitable for hourly ED of a large-scale grid.…”

Section: Ref Optimizationmentioning

confidence: 99%

“…Similarly, linearizing the second-stage objective function (9) gives additional inequality constraints expressed by (19) where r4 and r5 are parameters for objective function linearization and S2,t and S3,t are binary variables for objective function linearization at time t. Finally, nonlinear objective functions (1) and (3) are linearized to a set of equality constraints (9), (10), and inequality constraints (15)- (19), which are in the form of an MIP problem tractable for analytic method-based optimization solvers. Figure 5 presents the coupling of the two-stage ED strategy using a modular program architecture, as discussed in Section 2.…”

Section: Module (4)mentioning

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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“…The Joulia package in Julia provides a modelling framework for electricity systems that optimizes dispatch (Weibezahn & Kendziorski, 2019). The generation, storage, and transmission capacities have to be provided as exogenous parameters.…”

Section: Capacityexpansion Within the Broader Ecosystemmentioning

confidence: 99%

CapacityExpansion: A capacity expansion modeling framework in Julia

Kuepper¹,

Teichgraeber²,

Brandt³

2020

JOSS

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CapacityExpansion is a Julia (Bezanson, Edelman, Karpinski, & Shah, 2017) implementation of a scale-independent capacity expansion modelling framework. It provides an extensible, multi-carrier, simple-to-use generation and transmission capacity expansion model that allows users to address a diverse set of research questions in the area of energy systems planning and can be used to plan and validate energy systems at scales ranging from districts to entire global regions. CapacityExpansion provides simple integration of (clustered) time-series, geographical, cost, and technology input data. The software features a modular model setup and an investment and dispatch optimization that uses the JUMP modelling language (Dunning, Huchette, & Lubin, 2017). The software further provides an interface between the optimization result and further analysis.

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Illustrating the Benefits of Openness: A Large-Scale Spatial Economic Dispatch Model Using the Julia Language

Cited by 17 publications

References 24 publications

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

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

CapacityExpansion: A capacity expansion modeling framework in Julia

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