Hosting Capacity Calculations in Power Systems

The aim of this thesis is to investigate and calculate Hosting Capacity in power systems to identify the maximum amount of renewable energy resources that can be deployed. After connecting distributed generation to distribution system, the performance index of the distribution system will improve or deteriorate. The point which is between the acceptable deterioration and unacceptable deterioration is the hosting capacity. This research is specifically concerned with the effect of increasing distributed generation on the performance index in distribution networks and finding the maximum point of the acceptable deterioration. The hosting capacity is the amount of distributed generation that can be added to the distribution network without requiring additional upgrades in the network. This thesis presents a novel mathematical algorithm to determine the hosting capacity and determine the amounts of distribution generators that can be added to distribution networks. The two primary boundaries considered in finding hosting capacity are overvoltage and overloading. The investigation shows that the performance index of the system will deteriorate after connection of additional distributed generations until finding the optimal addition. The results show that the distribution system could accept more wind power with respect to its design criteria. The practicality of the proposed methodology is verified through simulations using two standard test systems: IEEE six-bus and IEEE 118-bus system

Hosting Capacity Optimization in Modern Distribution Grids

The availability of distributed renewable energy resources and the anticipated increase in new types of loads are changing the way electricity is being produced and supplied to consumers. This shift is moving away from a network delivering power solely from centralized power plants towards a decentralized network which supplements its power production by incorporating local distributed generators (DGs). However, the increased integration of DGs into existing distribution networks is impacting their behavior in terms of voltage profile, reliability, and power quality. To determine the maximum amount of DG that distribution grids can accommodate the concept of hosting capacity is introduced. The distribution grid hosting capacity is defined as the amount of new production or consumption that can be added to the grid without adversely impacting the reliability or voltage quality for other customers. The study of the hosting capacity is commonly accomplished by simulating power flow for each potential placement of DG while enforcing operating limits (e.g. voltage limits and line thermal limits). Traditionally, power flow is simulated by solving full nonlinear AC power flow equations for each potential configuration. Existing methods for computing hosting capacity require extensive iterations, which can be computationally-expensive and lack solution optimality. In this dissertation, several approaches for determining the optimal hosting capacity are introduced. First, an optimization-based method for determining the hosting capacity in distribution grids is proposed. The method is developed based on a set of linear power flow equations that enable linear programming formulation of the hosting capacity model. The optimization-based hosting capacity method is then extended to investigate further increasing hosting capacity by also optimizing network reconfiguration. The network reconfigurations use existing switches in the system to increase allowable hosting capacity without upgrading the network infrastructure. Finally, a sensitivity-based method is described which more efficiently obtains the optimal hosting capacity for larger distribution systems. The proposed methods are examined on several test radial distribution grids to show their effectiveness and acceptable performance. Performance is further measured against existing iterative hosting capacity calculation methods. Results demonstrate that the proposed method outperforms traditional methods in terms of computation time while offering comparable results

Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System

This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies