J. Hetzer

Bio: J. Hetzer is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Optimization problem & Wind speed. The author has an hindex of 1, co-authored 1 publications receiving 852 citations.

An Economic Dispatch Model Incorporating Wind Power

Abstract: In solving the electrical power systems economic dispatch (ED) problem, the goal is to find the optimal allocation of output power among the various generators available to serve the system load. With the continuing search for alternatives to conventional energy sources, it is necessary to include wind energy conversion system (WECS) generators in the ED problem. This paper develops a model to include the WECS in the ED problem, and in addition to the classic economic dispatch factors, factors to account for both overestimation and underestimation of available wind power are included. With the stochastic wind speed characterization based on the Weibull probability density function, the optimization problem is numerically solved for a scenario involving two conventional and two wind-powered generators. Optimal solutions are presented for various values of the input parameters, and these solutions demonstrate that the allocation of system generation capacity may be influenced by multipliers related to the risk of overestimation and to the cost of underestimation of available wind power.

Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization

Abstract: It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios.

Robust Energy Management for Microgrids With High-Penetration Renewables

Abstract: Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.

Optimal Allocation of ESS in Distribution Systems With a High Penetration of Wind Energy

Abstract: Environmental concerns and fuel cost uncertainties associated with the use of conventional energy sources have resulted in rapid growth in the amount of wind energy connected to distribution grids. However, based on Ontario's standard offer program (SOP), the utility has the right to curtail (spill) wind energy in order to avoid any violation of the system constraints. This means that any increase in wind energy production over a specific limit might be met with an increase in the wind energy curtailed. In spite of their cost, energy storage systems (ESSs) are considered to be a viable solution to this problem. This paper proposes a methodology for allocating an ESS in a distribution system with a high penetration of wind energy. The ultimate goal is to maximize the benefits for both the DG owner and the utility by sizing the ESS to accommodate all amounts of spilled wind energy and by then allocating it within the system in order to minimize the annual cost of the electricity. In addition, a cost/benefit analysis has been conducted in order to verify the feasibility of installing an ESS from the perspective of both the utility and the DG owner.

Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis

Abstract: This paper proposes a distributed mechanism for energy trading among microgrids in a competitive market. We consider multiple interconnected microgrids in a region where, at a given time, some microgrids have superfluous energy for sale or to keep in storage facilities, whereas some other microgrids wish to buy additional energy to meet local demands and/or storage requirements. Under our approach, sellers lead the competition by independently deciding the amount of energy for sale subject to a tradeoff between the attained satisfaction from the received revenue and that from the stored energy. Buyers follow the sellers' actions by independently submitting a unit price bid to the sellers. Correspondingly, the energy is allocated to the buyers in proportion to their bids, whereas the revenue is allocated to the sellers in proportion to their sales. We study the economic benefits of such an energy trading mechanism by analyzing its hierarchical decision-making scheme as a multileader–multifollower Stackelberg game. We show that distributing the energy based on a well-defined utility function converges to a unique equilibrium solution for maximizing the payoff of all participating microgrids. This game-theoretic study provides an incentive for energy trading among microgrids in future power grids.