Centrally Managed Storage Facilities in Small Non-Interconnected Island Systems

Optimal Operations Management of Hybrid Energy Systems Through Short-Term Atmospheric and Demand Forecasts

In this paper we present a new algorithm to find the optimal unit commitment and economic dispatch (UC/D) for a large hydrothermal power system. Mathematically, this is a large nonlinear, mixed integer optimization problem involving over 16,800 integer variables for a system having 100 thermal units with a 1-week scheduling horizon. We use a dual programming approach to solve a separate optimal control problem for each thermal unit and for each set of coupled hydro units. The algorithm has been tested on a 100 thermal unit/6 hydro unit system having a 168-hour scheduling period, and will find solutions that are within 0.1 to 0.25 percent of the optimal dual cost. We report computational results for a system modeled after the New York Power Pool.

Optimizing biogas plants with excess power unit and storage capacity in electricity and control reserve markets

Enhanced priority list unit commitment method for power systems with a high share of renewables

Stochastic Hopfield artificial neural network for unit commitment and economic power dispatch

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

Optimal dispatch of BESS-fed virtual power lines under transmission congestion and bulk renewable generation

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

Battery energy storage train routing and security constrained unit commitment under solar uncertainty

Microgrids (MGs) provide solutions to various problems in power systems by integrating non-renewable and renewable energy sources like combined heat and power (CHP) plants and distributed energy resources (DER). This paper has developed an optimization framework for economic dispatch of microgrids, including heat storage tanks and to increase the flexibility of CHP plants and boiler units. In addition, conventional power generators, intermittent energy resources such as photovoltaic (PV) units, wind turbines and battery energy storage system (BESS) are modelled in a modified IEEE 33 bus test system MG operating in islanded mode. Local generation and DER penetration can help system operators to satisfy energy demands with no need to purchase power from the grid. The main goal of the proposed paper is to optimally and economically dispatch heat and electricity considering impacts of heat buffer tank and battery energy storage, taking into account the technical constraints like AC/DC power flow limitations and the effects of dual dependency of produced heat and power by CHP units. The MG is studied within a time horizon of 24 hours and obtained results are analyzed for comparison.

This paper proposes a two-stage decision-making tool to assess the impacts of energy storage systems (ESSs) and offshore wind farms (OSW) integration in the power grid. To quantify the potential impacts, various key performance indicators (KPIs) are incorporated. These KPIs gage the environmental, technical, and economical attributes of the integrated system. The proposed framework uses a unit commitment (UC) mixed-integer linear programming (MILP) model. Two case studies (one for New Jersey and one for New York) are examined with clean power transition targets. The uncertainties of the net-load and power generation from renewable energy sources (RESs) are characterized by Gaussian Process Regression (GPR) models. These models are fine-tuned using the base forecasts generated using our in-house load forecasting tool and the National Renewable Energy Laboratory’s (NREL) publicly available generation calculator. The results show that ESS installations almost always improve the performance of the grid, regardless of the location and configuration. Furthermore, the unequally distributed ESS installations show better impacts than standalone centralized ESSs; and the mixed ESS technologies outperform single-type ESS deployments.

Guest Editorial: Situational awareness of integrated energy systems

Optimal scheduling of the battery energy storage (BES) in a microgrid is presented in this paper. The microgrid model in this paper consists of a photovoltaic (PV) power plant, a wind generator (WG), battery energy storage (BES), load, and diesel generator (DG). BES is required to store the generation excess from renewable energy sources (RES). BES also can store energy from the main grid but optimization is required to decide when to charge and discharge the battery. The optimization goal is to minimize electricity costs when the main grid is available and fuel costs when it is not available. The optimization problem in this paper is a linear programming (LP) problem that is modeled and solved with Python Gurobi. Optimization results are presented for different cases. A DG is used only when the main grid is not available due to the high fuel price. Electricity cost from DG is 5.28 times higher than the cost from the grid for the first case and 4.36 times higher for the second case. There is no generation excess in the second case so the battery is not used when the main grid is not available.

Unit Commitment (UC) is an essential decision taken by ISO to ensure reliable and economic operation of the power system. Renewable energy penetration is increasing in a power system due its inexhaustive and environment friendly nature. Renewable energy poses some challenges to the present power systems due to its high uncertainty. In addition to this, the impact of contingencies on the power system also cannot be ignored. Both renewable energy and contingencies make the power system more prone to uncertain behaviour which can affect the economics as well as reliability of the power system. In this paper, UC is solved using deterministic and stochastic approaches. Deterministic UC considers forecasted wind data and Stochastic UC considers uncertain scenarios of the wind. Practically the power system is more prone to uncertainty because of renewable energy penetration and contingencies. Thus, this paper discusses how a UC decision varies as more uncertainties arises. Mixed Integer Linear Programming (MILP) is used for obtaining the UC decision results for IEEE 30-bus system. The results of UC, power dispatch and generation costs obtained with the two approaches have been compared.

Distribution systems (DS) are a focal connection point for existing and new renewable energy sources. DS have increasing sizes; they connect increasing numbers of renewable energy sources and have a mandate to operate as financial entities seeking to maximize their profits in an electricity market. To achieve these goals, DS require highly efficient planning tools. In this paper, we propose a successive fuzzy mixed integer linear programming (MILP) method for optimally placing and sizing capacitors in a DS that explicitly models induction run wind electric generators (WEGs). The optimization problem is linearized, transformed into a fuzzy MILP model and is solved successively while updating the solution vector. The fuzzy MILP method employs fuzzy models of voltage constraints that are often violated in a planned system that lacks reactive power support. The use of fuzzy constraints makes the proposed solution method very robust by avoiding infeasibility and reducing the need for user expertise. Further, the fuzzy MILP method represents the dual objectives of capacitor cost and annual energy loss minimization as fuzzy sets with individual satisfaction functions. The method maximizes the minimum of these satisfaction functions during optimization. Tests on 69-bus and 2002-bus DS show that the method performs well and is robust.