Active Distribution Network Planning Considering Renewable Generation And Demand Uncertainties

THE deployment of distributed energy resource in its different forms and scales,such as the photovoltaics,wind turbines,combined-heat-and-power plants,electric vehicles and storage units,in the low-voltage power distribution networks has transformed the way these systems are planned and operated.Conventionally,distribution networks would have operated as a passive part of the power system,which supplied electricity to the consumers without the need of management and control schemes.However,the advent of distributed energy resources brings about unanticipated challenges for the distribution networks,because these resources were not accounted for during the preliminary planning of these networks.For instance,the intermittent generation of the non-dispatchable renewable distributed generation,along with the variability in the conventional loads,electric vehicle demand,demand growth and electricity prices,impose various operational uncertainties on the distribution networks.This thesis examines several planning solutions that ensure the accurate and efficient modelling of the aforementioned uncertainties to achieve the optimal integration of distributed energy resources in the active distribution networl The uncertainties are characterized by a scenario matrix based on the heuristic moment matching method that captures the stochastic features and the correlation among the historical demand and generation.The proposed scenario matrix characterizes the uncertainties using a reduced set of representative scenarios and hence,provides computational efficiency.The scenario matrix is then employed to propose a number of planning solutions that aim at various objectives,such as the mitigation of the active and reactive power losses,the voltage deviation,the investment and operational costs,and the carbon dioxide emissions.An analytical distributed generation placement approach is proposed as a preliminary tool for determining the maximum penetration of renewable distributed generators,such as the wind turbines and the solar photovoltaics in the active distribution network.Renewable distributed generators are the focus of this work,although this method could be applied to other types of generators.A simplified set of analytical expressions is first presented for the calculation of maximum distributed generation penetration at individual buses in the distribution network.Then,the solution realized by the analytical expressions is validated against the exhaustive load flow analysis in the 33-bus radial distribution test system.A distributed generation placement method is then developed based on the analytical expressions.The application of the distributed generation placement method is assessed in a 69-bus distribution system where the numerical results confirm the effectiveness as well as the benefits provided by the proposed formulation.Building upon the distributed generation placement approach,a scenario-based stochastic distributed generation planning model is proposed for the explicit contemplation of the uncertainties of wind and photovoltaic generation,and electricity demand.The proposed model has the capability to determine the optimal capacities and locations of the renewable distributed generators,such as the wind turbines and the photovoltaics,for a selected objective function subject to the mandatory constraints.The proposed model can,therefore,be employed for several applications.For the sake of this study,a multi-objective index is considered for the minimization of the active and reactive power losses,and the voltage deviation in the active distribution network.A scenario matrix is first generated using the heuristic moment matching method that takes into account the stochastic features(i.e.expectation,standard deviation and kurtosis)as well as the correlation among the historical wind and photovoltaic generation,and electricity demand.The scenario matrix effectively transforms the massive number of historical scenarios into a significantly reduced number of representative scenarios.The scenario matrix is subsequently employed to develop the stochastic planning model whose effectiveness is assessed through case-studies in the 53-bus and the IEEE 123-bus test distribution systems.The proposed planning model is based on mixed-integer non-linear programming due to the consideration of discrete DG sizes,where the non-linear constraints are processed using an exact second order cone programming relaxation,and then the relaxed model is implemented in YALMIP and solved in CPLEX.The superiority of the stochastic planning solution is confirmed through a comparative analysis against the deterministic planning solution,showing that the proposed solution substantially reduces the active and reactive power losses,and the voltage deviation.The proposed scenario matrix is further exploited to propose the scenario-based stochastic distributed generation investment planning model,considering the uncertainties of the wind and photovoltaic generation as well as the load demand and the electricity price.The proposed economic model employs a cost-benefit analysis to maximize the net present value from the perspective of the distribution network operator.The performance of the proposed model is firstly verified in the 53-bus distribution network and subsequently,its scalability is validated in the 138-bus distribution network.The numerical results confirm the economic significance of the proposed solution in terms of net present value maximization,whereas the comparative analysis against the deterministic planning solution demonstrates that the proposed model explicitly captures the network uncertainties.The performance of the proposed model is further assessed,showing that significant improvements in the net present value at increased levels of the distributed generation penetration.In order to integrate the distributed energy storage systems and exploit the associated arbitrage benefit,the scenario-based stochastic distributed generation investment planning model is further developed to identify the optimal mix,siting and sizing of the wind turbines,photovoltaics and battery units for maximizing the net present value of the distribution network operator.The effectiveness and the scalability of the proposed model is confirmed through case-studies in the 53-bus and the IEEE 123-bus distribution systems,respectively.The performance of the proposed planning solution is also validated using the deterministic planning solution as the comparison benchmark,demonstrating substantial improvement in the net present value as well as reductions in the bus voltage violations and unsupplied loads.A sensitivity analysis is also provided to assess the performance of the proposed planning solution,in terms of the commercially available battery energy capacities and power ratings as well as the load scales and the distributed generation penetration levels.Building upon the distributed generation investment planning approach,a scenario-based stochastic model is proposed for the multistage joint expansion planning of the distribution systems and the electric vehicle charging stations.The electric vehicle charging demand is firstly determined using a Markov-based temporal state-of-charge analysis of the driving patterns and charging demand.The scenario matrix is then employed to characterize the stochastic features and the correlation among historical wind and photovoltaic generation,as well as the conventional loads and electric vehicle demand.The proposed scenario-based expansion plan determines the optimal construction/reinforcement of substations,circuits and charging stations,along with the placement of distributed generators and capacitor banks,whilst aiming at the minimization of investment and operational costs.The effectiveness and the scalability of the proposed model is assessed through case-studies in the 18-bus and the IEEE 123-bus distribution systems,respectively.Numerical results confirm the effectiveness of the proposed approach for the minimization of costs as well as the adequate characterization of uncertainties.The comparative analysis of the proposed approach,against the deterministic and robust approaches,highlights its superiority in terms of handling the uncertainties associated with the renewable distributed generation and the electric vehicle demand.To investigate the multi-energy generation and storage technologies,a scenario-based multi-energy microgrid investment planning model is proposed,aiming at the minimization of the investment and operation costs,and the carbon dioxide emissions.The proposed planning solution determines the optimal distributed energy resource mix,siting and sizing in the autonomous microgrids.The distributed energy resource mix is comprised of the wind turbines,photovoltaics,battery storage units,gas-fired boilers,thermal storage tanks,combined-heat-and-power units,electric chillers and absorption chillers.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.The scenario matrix is employed to tackle the operational uncertainties associated with the wind and photovoltaic generation,and the electrical,heating and cooling loads.The numerical results obtained from the case-study in the 19-bus microgrid test system demonstrate significant reductions in the investment and operation costs as well as the carbon dioxide emissions.The superiority of the proposed planning solution is further validated using the deterministic planning solution as the comparison benchmark.