The Experts below are selected from a list of 76416 Experts worldwide ranked by ideXlab platform
Douglas J Gotham - One of the best experts on this subject based on the ideXlab platform.
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long term Investment Planning for the electricity sector in small island developing states case study for jamaica
Energy, 2021Co-Authors: Travis R Atkinson, Paul V Preckel, Douglas J GothamAbstract:Abstract Long term infrastructure Investment Planning for the electricity sector in Small Island Developing States typically optimizes generation and transmission Investments sequentially. Compared to a simultaneous Planning method, the current practice may result in a misallocation of scarce resources. To address this, this paper makes two contributions. First, it presents a framework for assessing two features of long-term Planning models while accounting for economic and geographic idiosyncrasies of small island states. These are: i) the simultaneous vs sequential treatment of generation and transmission Investments and ii) the impact of loop flow (a phenomenon intrinsic to electricity transmission networks) on long-term Investment Planning. Second, it quantifies the magnitude of omitting these model features using Jamaica as a test case. Depending on the initial conditions of the network, a simultaneous Planning approach yields cost-efficiency gains in the order of 3.3%–3.6%. This is substantial when converted to financial costs and excess infrastructure Investments. Importantly, energy modelers may want to think carefully about whether or not their results are liable to suffer from omitting these features and by making data and program codes publicly available, this paper broadens the scope for energy economic research in small island states.
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a mathematical programming formulation for long term infrastructure Investment Planning in small island developing states
MethodsX, 2021Co-Authors: Travis R Atkinson, Paul V Preckel, Douglas J GothamAbstract:Abstract Mixed-integer programming is a common method used in electricity generation and transmission optimization models. However, the size of the problem can result in extraordinarily long run times. Solve time also increases exponentially with the number of variables to optimize. There is therefore a constant trade-off between a realistic representation of the network and computational tractability. Additionally, actual data and publicly available, real-world application are scare. This is particularly true for Small Island Developing States. This paper bridges these gaps by describing a customized mathematical formulation for co-optimizing generation and transmission infrastructure Investments. Data from the island of Jamaica and program scripts are available for reproduction. Key customizations to a mixed-integer programming model for long-term generation and transmission infrastructure Investment Planning include: • Hours are treated as representative hour categories and multiplied by the number of hour types within a given period. • Simulated construction is limited to every other year. • While fossil fuel plants are treated as discrete variables, renewable energy plants are treated as continuous variables.
Qiang Yang - One of the best experts on this subject based on the ideXlab platform.
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scenario based Investment Planning of isolated multi energy microgrids considering electricity heating and cooling demand
Applied Energy, 2019Co-Authors: Ali Ehsan, Qiang YangAbstract:Abstract Multi-energy microgrids provide a flexible solution for the utilization of the distributed energy resources in order to meet the electrical, heating and cooling energy demands in the off-grid communities. However, the Planning of the multi-energy microgrids is a non-trivial problem due to the complex energy flows between the sources and the loads pertaining to the electrical, heating and cooling energy, along with the intermittency of the renewable distributed generation. This work proposes a scenario-based stochastic multi-energy microgrid Investment Planning model that aims to minimize the Investment and operation costs as well as the Carbon dioxide emissions by determining the optimal distributed energy resource mix, siting and sizing in the isolated microgrids. The proposed Planning model employs the power flow and heat transfer equations to explicitly model the energy flows between electrical, heating and cooling energy sources and loads. Moreover, an uncertainty matrix is employed to tackle the operational uncertainties associated with the wind and photovoltaic generation, and the electrical, heating and cooling loads. The uncertainty matrix is modeled using the heuristic moment matching method that effectively captures the stochastic moments and correlation among the historical scenarios. The numerical results obtained from the case-study in the 19-bus microgrid test system confirm that the proposed methodology provides significant reductions in the Investment and operation costs as well as the Carbon dioxide emissions. Finally, the superiority of the proposed Planning solution is also validated using the deterministic Planning solution as the comparison benchmark.
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Coordinated Investment Planning of Distributed Multi-Type Stochastic Generation and Battery Storage in Active Distribution Networks
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Ali Ehsan, Qiang YangAbstract:This paper presents a scenario-based stochastic active distribution network Planning (ADNP) model considering the multi-type distributed generation and battery energy storage (BES). The proposed solution aims to identify the optimal mix, siting, and sizing of wind turbine (WT), photovoltaic (PV), and BES units to maximize the net present value of distribution network operator (DNO) while fully exploiting the BES arbitrage benefit. First, a heuristic moment matching based uncertainty matrix comprising of representative scenarios is generated to effectively capture the stochastic characteristics and correlation among historical WT generation, PV generation, and load demand. Then, the uncertainty matrix is incorporated to formulate the stochastic ADNP problem. The proposed solution minimizes the costs and maximizes the revenues of the DNO. The effectiveness and scalability of the proposed model are evaluated through case studies in the 53-bus and IEEE 123-bus distribution systems. Finally, the performance of the proposed model is compared against the deterministic Planning model, and a sensitivity analysis is performed to assess the impact of various Planning factors on the proposed solution.
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stochastic Investment Planning model of multi energy microgrids considering network operational uncertainties
China International Conference on Electricity Distribution, 2018Co-Authors: Ali Ehsan, Qiang YangAbstract:This work proposes a scenario-based stochastic microgrid Investment Planning model in the presence of various forms of generation and demand with operational uncertainties. The solution aims to minimize the overall cost and carbon dioxide emissions in microgrid through determining the optimal placement and capacities (i.e. siting and sizing) of the distributed energy resources (DERs). The DER mix comprises of the wind turbines., photovoltaics., gas-boiler., and combined heat and power units. The proposed Planning model is based on linear power flow and heat transfer equations, and explicitly captures the interaction between electricity and heating DERs. To address the operational uncertainties associated with the wind and photovoltaic generation as well as the electricity and heating demands, an uncertainty matrix is adopted. The uncertainty matrix is generated using the heuristic moment matching (HMM) method that effectively captures the stochastic moments and correlation among the historical data. The numerical results from a case-study on 19-bus microgrid test system confirm the effectiveness of the proposed model.
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robust active distribution network Planning considering stochastic renewable distributed generation
Chinese Control Conference, 2018Co-Authors: Jianhua Yao, Ali Ehsan, Ming Cheng, Qiang YangAbstract:Investment in distributed generation (DG) is an attractive Planning option for enhancing the reliability and security of power distribution systems. However, uncertainties due to intermittent generation of renewable energy based DGs i.e. wind turbines (WTs) and solar photovoltaics (PVs) are a challenge for DG integration. This work proposes a robust distributed generation Investment Planning (DGIP) which considers the uncertainties associated with the intermittent renewable generation and variable electricity demand. The proposed DGIP minimizes the net present value (NPV) of total costs of Investment, operation, maintenance and emissions of WTs and PVs. The proposed Planning solution is assessed using the 53-bus test feeder and the numerical result demonstrates its benefit against the conventional solution.
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A scenario-based robust Investment Planning model for multi-type distributed generation under uncertainties
IET Generation Transmission & Distribution, 2018Co-Authors: Ali Ehsan, Qiang Yang, Ming ChengAbstract:This paper presented a scenario-based robust distributed generation Investment Planning (DGIP) model, which considered the uncertainties of wind turbine (WT) generation, photovoltaic (PV) generation and load demand. The robust economic model aims to maximize the net present value (NPV) from the distribution network operator's (DNO's) perspective. The uncertainties are described by an uncertainty matrix based on a heuristic moment matching (HMM) method that captures the stochastic features, i.e. expectation, standard deviation, skewness and kurtosis. The notable feature of the HMM method is that it diminishes the computational burden considerably by representing the uncertainties through a reduced number of representative scenarios. The uncertainty matrix is integrated with deterministic power flow equations to formulate a cost-benefit analysis based robust DGIP model with the objective of maximizing the DNO's net present value. The effectiveness of the proposed DGIP model is firstly verified in a 53-bus distribution test feeder, and then its scalability is further validated in a 138-bus distribution network. The numerical results confirm that the proposed DGIP solution is more robust for all representative network scenarios against the deterministic solution.
Ali Ehsan - One of the best experts on this subject based on the ideXlab platform.
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scenario based Investment Planning of isolated multi energy microgrids considering electricity heating and cooling demand
Applied Energy, 2019Co-Authors: Ali Ehsan, Qiang YangAbstract:Abstract Multi-energy microgrids provide a flexible solution for the utilization of the distributed energy resources in order to meet the electrical, heating and cooling energy demands in the off-grid communities. However, the Planning of the multi-energy microgrids is a non-trivial problem due to the complex energy flows between the sources and the loads pertaining to the electrical, heating and cooling energy, along with the intermittency of the renewable distributed generation. This work proposes a scenario-based stochastic multi-energy microgrid Investment Planning model that aims to minimize the Investment and operation costs as well as the Carbon dioxide emissions by determining the optimal distributed energy resource mix, siting and sizing in the isolated microgrids. The proposed Planning model employs the power flow and heat transfer equations to explicitly model the energy flows between electrical, heating and cooling energy sources and loads. Moreover, an uncertainty matrix is employed to tackle the operational uncertainties associated with the wind and photovoltaic generation, and the electrical, heating and cooling loads. The uncertainty matrix is modeled using the heuristic moment matching method that effectively captures the stochastic moments and correlation among the historical scenarios. The numerical results obtained from the case-study in the 19-bus microgrid test system confirm that the proposed methodology provides significant reductions in the Investment and operation costs as well as the Carbon dioxide emissions. Finally, the superiority of the proposed Planning solution is also validated using the deterministic Planning solution as the comparison benchmark.
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Coordinated Investment Planning of Distributed Multi-Type Stochastic Generation and Battery Storage in Active Distribution Networks
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Ali Ehsan, Qiang YangAbstract:This paper presents a scenario-based stochastic active distribution network Planning (ADNP) model considering the multi-type distributed generation and battery energy storage (BES). The proposed solution aims to identify the optimal mix, siting, and sizing of wind turbine (WT), photovoltaic (PV), and BES units to maximize the net present value of distribution network operator (DNO) while fully exploiting the BES arbitrage benefit. First, a heuristic moment matching based uncertainty matrix comprising of representative scenarios is generated to effectively capture the stochastic characteristics and correlation among historical WT generation, PV generation, and load demand. Then, the uncertainty matrix is incorporated to formulate the stochastic ADNP problem. The proposed solution minimizes the costs and maximizes the revenues of the DNO. The effectiveness and scalability of the proposed model are evaluated through case studies in the 53-bus and IEEE 123-bus distribution systems. Finally, the performance of the proposed model is compared against the deterministic Planning model, and a sensitivity analysis is performed to assess the impact of various Planning factors on the proposed solution.
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stochastic Investment Planning model of multi energy microgrids considering network operational uncertainties
China International Conference on Electricity Distribution, 2018Co-Authors: Ali Ehsan, Qiang YangAbstract:This work proposes a scenario-based stochastic microgrid Investment Planning model in the presence of various forms of generation and demand with operational uncertainties. The solution aims to minimize the overall cost and carbon dioxide emissions in microgrid through determining the optimal placement and capacities (i.e. siting and sizing) of the distributed energy resources (DERs). The DER mix comprises of the wind turbines., photovoltaics., gas-boiler., and combined heat and power units. The proposed Planning model is based on linear power flow and heat transfer equations, and explicitly captures the interaction between electricity and heating DERs. To address the operational uncertainties associated with the wind and photovoltaic generation as well as the electricity and heating demands, an uncertainty matrix is adopted. The uncertainty matrix is generated using the heuristic moment matching (HMM) method that effectively captures the stochastic moments and correlation among the historical data. The numerical results from a case-study on 19-bus microgrid test system confirm the effectiveness of the proposed model.
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robust active distribution network Planning considering stochastic renewable distributed generation
Chinese Control Conference, 2018Co-Authors: Jianhua Yao, Ali Ehsan, Ming Cheng, Qiang YangAbstract:Investment in distributed generation (DG) is an attractive Planning option for enhancing the reliability and security of power distribution systems. However, uncertainties due to intermittent generation of renewable energy based DGs i.e. wind turbines (WTs) and solar photovoltaics (PVs) are a challenge for DG integration. This work proposes a robust distributed generation Investment Planning (DGIP) which considers the uncertainties associated with the intermittent renewable generation and variable electricity demand. The proposed DGIP minimizes the net present value (NPV) of total costs of Investment, operation, maintenance and emissions of WTs and PVs. The proposed Planning solution is assessed using the 53-bus test feeder and the numerical result demonstrates its benefit against the conventional solution.
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A scenario-based robust Investment Planning model for multi-type distributed generation under uncertainties
IET Generation Transmission & Distribution, 2018Co-Authors: Ali Ehsan, Qiang Yang, Ming ChengAbstract:This paper presented a scenario-based robust distributed generation Investment Planning (DGIP) model, which considered the uncertainties of wind turbine (WT) generation, photovoltaic (PV) generation and load demand. The robust economic model aims to maximize the net present value (NPV) from the distribution network operator's (DNO's) perspective. The uncertainties are described by an uncertainty matrix based on a heuristic moment matching (HMM) method that captures the stochastic features, i.e. expectation, standard deviation, skewness and kurtosis. The notable feature of the HMM method is that it diminishes the computational burden considerably by representing the uncertainties through a reduced number of representative scenarios. The uncertainty matrix is integrated with deterministic power flow equations to formulate a cost-benefit analysis based robust DGIP model with the objective of maximizing the DNO's net present value. The effectiveness of the proposed DGIP model is firstly verified in a 53-bus distribution test feeder, and then its scalability is further validated in a 138-bus distribution network. The numerical results confirm that the proposed DGIP solution is more robust for all representative network scenarios against the deterministic solution.
Travis R Atkinson - One of the best experts on this subject based on the ideXlab platform.
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long term Investment Planning for the electricity sector in small island developing states case study for jamaica
Energy, 2021Co-Authors: Travis R Atkinson, Paul V Preckel, Douglas J GothamAbstract:Abstract Long term infrastructure Investment Planning for the electricity sector in Small Island Developing States typically optimizes generation and transmission Investments sequentially. Compared to a simultaneous Planning method, the current practice may result in a misallocation of scarce resources. To address this, this paper makes two contributions. First, it presents a framework for assessing two features of long-term Planning models while accounting for economic and geographic idiosyncrasies of small island states. These are: i) the simultaneous vs sequential treatment of generation and transmission Investments and ii) the impact of loop flow (a phenomenon intrinsic to electricity transmission networks) on long-term Investment Planning. Second, it quantifies the magnitude of omitting these model features using Jamaica as a test case. Depending on the initial conditions of the network, a simultaneous Planning approach yields cost-efficiency gains in the order of 3.3%–3.6%. This is substantial when converted to financial costs and excess infrastructure Investments. Importantly, energy modelers may want to think carefully about whether or not their results are liable to suffer from omitting these features and by making data and program codes publicly available, this paper broadens the scope for energy economic research in small island states.
-
a mathematical programming formulation for long term infrastructure Investment Planning in small island developing states
MethodsX, 2021Co-Authors: Travis R Atkinson, Paul V Preckel, Douglas J GothamAbstract:Abstract Mixed-integer programming is a common method used in electricity generation and transmission optimization models. However, the size of the problem can result in extraordinarily long run times. Solve time also increases exponentially with the number of variables to optimize. There is therefore a constant trade-off between a realistic representation of the network and computational tractability. Additionally, actual data and publicly available, real-world application are scare. This is particularly true for Small Island Developing States. This paper bridges these gaps by describing a customized mathematical formulation for co-optimizing generation and transmission infrastructure Investments. Data from the island of Jamaica and program scripts are available for reproduction. Key customizations to a mixed-integer programming model for long-term generation and transmission infrastructure Investment Planning include: • Hours are treated as representative hour categories and multiplied by the number of hour types within a given period. • Simulated construction is limited to every other year. • While fossil fuel plants are treated as discrete variables, renewable energy plants are treated as continuous variables.
James D. Mccalley - One of the best experts on this subject based on the ideXlab platform.
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modeling operational effects of wind generation within national long term infrastructure Planning software
IEEE Transactions on Power Systems, 2013Co-Authors: Venkat Krishnan, Trishna Das, Eduardo Ibanez, Cristian A. Lopez, James D. MccalleyAbstract:This paper describes new Investment Planning software which is multi-sector (fuels, electric, and transportation), multiobjective, national, and long-term (40 years) that identifies a set of non-dominated national Investment strategies. It optimizes three objectives: cost, emissions, and system resilience to major disruptions such as the Katrina and Rita hurricanes. Solutions are identified in terms of technologies (generation, transmission, fuel infrastructure, and transportation infrastructure), capacity, Investment year, and geographic location. Network topology is respected. This paper focuses on modeling operational effects of growing wind generation in terms of regulation, reserves, ramping capability and capacity, and their influence on Planning the future generation portfolios.
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national long term Investment Planning for energy and transportation systems
Power and Energy Society General Meeting, 2010Co-Authors: James D. Mccalley, Eduardo Ibanez, Konstantina Gkritza, Dionysios C Aliprantis, Lizhi Wang, Arun K Somani, Robert C BrownAbstract:The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases in the US today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting, and converting energy for electric and transportation systems are numerous. Selecting from among them requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. The advent of electrified transportation creates interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modeling capability to perform long-term electric/transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow/DC-flow modeling with a multiobjective solution method. We motivate the need for this work by summarizing attributes and issues related to the Investment Planning problem so as to find minimum-cost, low-emission, resilient infrastructure portfolios for the future. State-of-the-art energy Planning models are summarized, and we describe our software design which includes a multiobjective evolutionary algorithm with a network linear programming cost minimization fitness evaluation, together with metrics for evaluating resiliency and sustainability.
