Coordinated Investment in Transmission and Storage Systems Representing Long- and Short-Term Uncertainty

Zhang, Xuan; Conejo, Antonio J.

doi:10.1109/tpwrs.2018.2842045

Cited by 69 publications

(48 citation statements)

References 15 publications

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“…Nevertheless, only a few vertices produce valid pieces in (9) and (11), but they are not known in advance. We propose a heuristic method; the flow chart is summarized in Algorithm 1.…”

Section: An Approximate Algorithm For Solving Mp-lpmentioning

confidence: 99%

“…To validate this OVF, we discretize K into 101 × 101 uniformly distributed points, and solve problem (5) for each point; the corresponding optimum v p (θ) is exact. Among the 101 × 101 samples, the maximum relative error |v p (θ) − v(θ)|/v p (θ) = 0.0011, because of missing some vertex µ in (11). Nevertheless, the uniform discretization is hopeful to allocate most active vertices that corresponds to a slice in the PWL function v(θ), so the relative error is generally very small.…”

Section: B Visualization Of the Ovfmentioning

confidence: 99%

“…Without the information on the probability distribution, a robust optimization method is proposed in [10] to size ESU in power grids, in which the uncertainty set is the convex hull of several extreme scenarios restricting the range of uncertain data. The robust optimization (RO) model is further developed in [11] to coordinate the investment in transmission lines and ESUs and consider longterm and short-term uncertainties. Although RO approach requires very little distributional information on uncertainty and tackles the worst-case situation, it usually provides conservative results because the worst case rarely happens.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Energy Storage on Economic Dispatch of Distribution Systems: A Multi-Parametric Linear Programming Approach and its Implications

Wei

Wang

et al. 2020

IEEE Open J. Power Energy

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“…Nevertheless, only a few vertices produce valid pieces in (9) and (11), but they are not known in advance. We propose a heuristic method; the flow chart is summarized in Algorithm 1.…”

Section: An Approximate Algorithm For Solving Mp-lpmentioning

confidence: 99%

Section: B Visualization Of the Ovfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Energy Storage on Economic Dispatch of Distribution Systems: A Multi-Parametric Linear Programming Approach and its Implications

Wei

Wang

et al. 2020

IEEE Open J. Power Energy

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“…Examples of continuous storage investment decisions include [14,[111][112][113][114][115]. Storage investment decisions modelled as binary variables can be found in [109,116,117].…”

Section: Ess Investment Modellingmentioning

confidence: 99%

Operating and Investment Models for Energy Storage Systems

2020

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In the context of climate changes and the rapid growth of energy consumption, intermittent renewable energy sources (RES) are being predominantly installed in power systems. It has been largely elucidated that challenges that RES present to the system can be mitigated with energy storage systems (ESS). However, besides providing flexibility to intermittent RES, ESS have other sources of revenue, such as price arbitrage in the markets, balancing services, and reducing the cost of electricity procurement to end consumers. In order to operate the ESS in the most profitable way, it is often necessary to make optimal siting and sizing decisions, and to determine optimal ways for the ESS to participate in a variety of energy and ancillary service markets. As a result, many publications on ESS models with various goals and operating environments are available. This paper aims at presenting the results of these papers in a structured way. A standard ESS model is first outlined, and that is followed by a literature review on operational and investment ESS models at the transmission and distribution levels. Both the price taking and price making models are elaborated on and presented in detail. Based on the examined body of work, the paper is concluded with recommendations for future research paths in the analysis of ESS.

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“…Then, a multi-objective transmission network expansion planning model considering the investment cost, risk cost, and congestion cost of the transmission network is established. In [36,37], the stochastic programming theory and scenario analysis method are used to describe the uncertainty of power generation, investment, and load demand of the transmission network on a long-time scale, as well as the uncertainty of distributed generation and load on a short-time scale. Then, the joint planning models of transmission network and energy storage are established.…”

Section: Introductionmentioning

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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Coordinated Investment in Transmission and Storage Systems Representing Long- and Short-Term Uncertainty

Cited by 69 publications

References 15 publications

Impact of Energy Storage on Economic Dispatch of Distribution Systems: A Multi-Parametric Linear Programming Approach and its Implications

Impact of Energy Storage on Economic Dispatch of Distribution Systems: A Multi-Parametric Linear Programming Approach and its Implications

Operating and Investment Models for Energy Storage Systems

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

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