The Experts below are selected from a list of 59598 Experts worldwide ranked by ideXlab platform

Goran Strbac - One of the best experts on this subject based on the ideXlab platform.

  • transmission Network Investment with distributed energy resources and distributionally robust security
    IEEE Transactions on Power Systems, 2019
    Co-Authors: Diego Alvarado, Rodrigo Moreno, Alexandre Moreira, Goran Strbac
    Abstract:

    Distributed energy resources (DER) have the potential to significantly contribute to Network security and hence release latent capacity of existing transmission assets. In this context, we propose a distributionally robust approach to Network security in order to recognize the limited data and knowledge associated with the underlying process behind the realization of system contingencies within the transmission expansion planning (TEP) problem, and thus determine the optimal portfolio of DER services necessary to displace, in a secure fashion, inefficient Network Investments. To do so, we propose a two-stage optimization model where the first stage determines the transmission expansion plan and the scheduling of DER post-contingency services in coordination with further corrective control measures such as generation reserves. The second stage minimizes the expected cost of corrective actions under various contingencies. Through various case studies, we demonstrate the benefits of security services provided by DER and the advantages of our proposed distributionally robust approach (where outage rates are assumed ambiguous) against alternative $n-K$ security and stochastic approaches, where outage rates are either ignored or assumed fully known, respectively.

  • a five level milp model for flexible transmission Network planning under uncertainty a min max regret approach
    IEEE Transactions on Power Systems, 2018
    Co-Authors: Alexandre Moreira, Goran Strbac, Rodrigo Moreno, Alexandre Street, Ioannis Konstantelos
    Abstract:

    The benefits of new transmission Investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning problem under generation expansion uncertainty in a min–max regret fashion, when considering flexible Network options and $n-1$ security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal Network Investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the five-level model, we present a cutting plane algorithm that ultimately identifies the optimal min–max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible Network Investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information methods, and (c) the computational scalability of the proposed approach.

  • Planning With Multiple Transmission and Storage Investment Options Under Uncertainty: A Nested Decomposition Approach
    IEEE Transactions on Power Systems, 2018
    Co-Authors: Paola Falugi, Ioannis Konstantelos, Goran Strbac
    Abstract:

    Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of Network Investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies, such as energy storage, can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these Network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale mixed integer linear programming problems that contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this paper, we propose a novel, efficient, and highly generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of nonsequential Investment state equations and subproblem nonconvexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty. The proposed method is shown to substantially outperform the current state of the art.

  • transmission Network Investment with probabilistic security and corrective control
    IEEE Transactions on Power Systems, 2013
    Co-Authors: Rodrigo Moreno, Danny Pudjianto, Goran Strbac
    Abstract:

    This paper demonstrates that the growth in application of corrective actions to enhance Network utilization will require a probabilistic treatment of Network security for determining efficient levels of Investment in Network reinforcement. A Benders decomposition based two-stage probabilistic optimization model for the operational and Investment problems is proposed. For selecting relevant contingencies (beyond N-1 criteria), a novel filtering technique for efficient elimination of redundant outages is presented and successfully tested. In 2 numerical examples we compare efficiency of Network reinforcement propositions under both deterministic and probabilistic frameworks, while optimizing available preventive and corrective control actions, and in particular focusing on the application of generation reserve in combination with special protection schemes (SPS) for Network congestion management purposes. We highlight the inadequacies of the deterministic approach with respect to its inherent inability to optimize accurately the portfolio of pre-fault post-fault actions since the impacts of corrective actions (in the form of SPS, demand response) and occurrence of “non-credible” events require explicit consideration of the likelihood of various outages. We conclude that deterministic approach drives less efficient and potentially more risky system operation that ultimately leads to inefficient Network Investment.

  • smart control for minimizing distribution Network reinforcement cost due to electrification
    Energy Policy, 2013
    Co-Authors: Danny Pudjianto, Predrag Djapic, Marko Aunedi, Goran Strbac, Sikai Huang, David Infield
    Abstract:

    Electrification of transport fleets and heating sectors is seen as one of the key strategies to further reduce the use of fossil fuels and the resulting greenhouse gas emissions. However, it will potentially cause a significant increase of electricity peak demand and have adverse consequences on the electricity system, in particular on distribution Networks. This paper will address the benefits of various applications of smart Network control and demand response technologies for enhancing the integration of these future load categories, and for improvements in operation management and efficient use of distribution Network assets. A range of numerical simulations have been carried out on different distribution Network topologies (rural and urban Networks) to identify the need and the cost of Network reinforcement required to accommodate future load under various operating strategies such as Business as Usual (passive demand and passive Network) against the smart grid approach. Applications of smart Plug-in vehicle (PiV) charging, smart heat pumps, and optimised control of Network voltage regulators to reduce Network Investment have been studied, and selected key results of our studies on evaluating the benefits of implementing these technologies for Great Britain's distribution Networks are presented and discussed in this paper.

Xi Tian - One of the best experts on this subject based on the ideXlab platform.

Lennart Söder - One of the best experts on this subject based on the ideXlab platform.

Rodrigo Moreno - One of the best experts on this subject based on the ideXlab platform.

  • transmission Network Investment with distributed energy resources and distributionally robust security
    IEEE Transactions on Power Systems, 2019
    Co-Authors: Diego Alvarado, Rodrigo Moreno, Alexandre Moreira, Goran Strbac
    Abstract:

    Distributed energy resources (DER) have the potential to significantly contribute to Network security and hence release latent capacity of existing transmission assets. In this context, we propose a distributionally robust approach to Network security in order to recognize the limited data and knowledge associated with the underlying process behind the realization of system contingencies within the transmission expansion planning (TEP) problem, and thus determine the optimal portfolio of DER services necessary to displace, in a secure fashion, inefficient Network Investments. To do so, we propose a two-stage optimization model where the first stage determines the transmission expansion plan and the scheduling of DER post-contingency services in coordination with further corrective control measures such as generation reserves. The second stage minimizes the expected cost of corrective actions under various contingencies. Through various case studies, we demonstrate the benefits of security services provided by DER and the advantages of our proposed distributionally robust approach (where outage rates are assumed ambiguous) against alternative $n-K$ security and stochastic approaches, where outage rates are either ignored or assumed fully known, respectively.

  • the value of Network Investment coordination to reduce environmental externalities when integrating renewables case on the chilean transmission Network
    Energy Policy, 2019
    Co-Authors: Carlos Matamala, Rodrigo Moreno, Enzo Sauma
    Abstract:

    Abstract The need to decarbonize the power sector through increased participation of renewable generation has originated an escalating necessity for transmission Network Investments that can be undertaken by a number of market participants, including planning authorities/system operators, Network companies and project developers. The expansion of the power Network, however, presents various environmental and social conflicts, in particular, with land uses that are valuable by society such as the presence of communities, national parks, protected forests, tourism zones, archaeological sites, etc. In this context of environmental and social awareness, we assess the benefits of two strategies that coordinate Network Investments among various participants and compare them against the current counterfactual approach, where no coordination is undertaken and thus renewable generation projects are connected to the main transmission system in an individual, project-by-project basis. Through various case studies based on the main Chilean transmission system, we show that the lack of coordination in Network Investments may present severe impacts in terms of the socio-environmental externalities of transmission Network expansions. Furthermore, we demonstrate that attempting to reduce externalities of new Network Investments without proper coordination of new developments may significantly limit the success of a land use policy associated with Network developments.

  • a five level milp model for flexible transmission Network planning under uncertainty a min max regret approach
    IEEE Transactions on Power Systems, 2018
    Co-Authors: Alexandre Moreira, Goran Strbac, Rodrigo Moreno, Alexandre Street, Ioannis Konstantelos
    Abstract:

    The benefits of new transmission Investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning problem under generation expansion uncertainty in a min–max regret fashion, when considering flexible Network options and $n-1$ security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal Network Investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the five-level model, we present a cutting plane algorithm that ultimately identifies the optimal min–max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible Network Investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information methods, and (c) the computational scalability of the proposed approach.

  • transmission Network Investment with probabilistic security and corrective control
    IEEE Transactions on Power Systems, 2013
    Co-Authors: Rodrigo Moreno, Danny Pudjianto, Goran Strbac
    Abstract:

    This paper demonstrates that the growth in application of corrective actions to enhance Network utilization will require a probabilistic treatment of Network security for determining efficient levels of Investment in Network reinforcement. A Benders decomposition based two-stage probabilistic optimization model for the operational and Investment problems is proposed. For selecting relevant contingencies (beyond N-1 criteria), a novel filtering technique for efficient elimination of redundant outages is presented and successfully tested. In 2 numerical examples we compare efficiency of Network reinforcement propositions under both deterministic and probabilistic frameworks, while optimizing available preventive and corrective control actions, and in particular focusing on the application of generation reserve in combination with special protection schemes (SPS) for Network congestion management purposes. We highlight the inadequacies of the deterministic approach with respect to its inherent inability to optimize accurately the portfolio of pre-fault post-fault actions since the impacts of corrective actions (in the form of SPS, demand response) and occurrence of “non-credible” events require explicit consideration of the likelihood of various outages. We conclude that deterministic approach drives less efficient and potentially more risky system operation that ultimately leads to inefficient Network Investment.

Yalin Huang - One of the best experts on this subject based on the ideXlab platform.