Abdollah Ahmadi

Mixed integer programming of multiobjective hydro-thermal self scheduling

This paper presents a method for hydro-thermal self scheduling (HTSS) problem in a day-ahead joint energy and reserve market. The HTSS is modeled in the form of multiobjective framework to simultaneously maximize GENCOs profit and minimize emissions of thermal units. In the proposed model the valve loading effects which is a nonlinear problem by itself is linearized. Also a dynamic ramp rate of thermal units is used instead of a fix rate leading to more realistic formulation of HTSS. Furthermore, the multi performance curves of hydro units is developed and prohibited operating zones (POZs) of thermal unit are considered in HTSS problem. Also, in the proposed framework, the mixed integer nonlinear programming (MINLP) of HTSS is converted to mixed integer programming (MIP) problem that can be effectively solved by optimization softwares even for real size power systems. The lexicographic optimization and hybrid augmented-weighted @?-constraint technique is implemented to generate Pareto optimal solutions. The best compromised solution is adopted either by using a fuzzy approach or by considering arbitrage opportunities to achieve more profit. Finally, the effectiveness of the proposed method is studied based on the IEEE 118-bus system.

MIP-Based Stochastic Security-Constrained Daily Hydrothermal Generation Scheduling

This paper presents the application of a mixed-integer programming (MIP) approach for solving stochastic security-constrained daily hydrothermal generation scheduling (SCDHGS). Power system uncertainties including generating units and branch contingencies and load uncertainty are explicitly considered in the stochastic programming of SCDHGS. The roulette wheel mechanism and lattice Monte Carlo simulation (LMCS) are first employed for random scenario generation wherein the stochastic SCDHGS procedure is converted into its respective deterministic equivalents (scenarios). Then, the generating units are scheduled through MIP over the set of deterministic scenarios for the purpose of minimizing the cost of supplying energy and ancillary services over the optimization horizon (24 h) while satisfying all the operating and network security constraints. To a more realistic modeling of the DHGS problem, in the proposed MIP formulation, the nonlinear valve loading effect, cost, and emission function are modeled in linear form, and prohibited operating zones (POZs) of thermal units are considered. Furthermore, a dynamic ramp rate of thermal units is used, and for the hydro plants, the multiperformance curve with spillage and time delay between reservoirs is considered. To assess the efficiency and powerful performance of the aforementioned method, a typical case study based on the standard IEEE-118 bus system is investigated, and the results are compared to each other in different test systems.

A Lexicographic Optimization and Augmented ϵ-constraint Technique for Short-term Environmental/Economic Combined Heat and Power Scheduling

Abstract—In recent years, combined heat and power units have become significant elements in conventional power stations due their numerous merits, including operational cost savings and reduced emissions. In this regard, this article proposes a short-term multi-objective framework for the combined heat and power economic/emission dispatch problem. In addition, to more precisely model the problem, the non-linear forms of fuel cost functions and valve-point loading along with power transmission loss are considered. The objectives of the problem are total cost minimization as well as minimization of pollutant emissions; lexicographic optimization and the augmented epsilon-constraint technique are employed to solve the multi-objective problem. Also, a fuzzy decision making technique has been used to select the most preferred solution among the Pareto solutions. Afterward, a comprehensive comparison is performed between the results obtained from the proposed method and those derived from the non-dominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2, and multi-objective line-up competition algorithm, verifying the superiority of the presented approach for lower execution time, total cost, and emission. Furthermore, the proposed model is implemented on a large-scale test system while the execution time is rational.

Optimal Robust Unit Commitment of CHP Plants in Electricity Markets Using Information Gap Decision Theory

This paper proposes a novel method based on information gap decision theory to evaluate a profitable operation strategy for combined heat and power units in a liberalized electricity market. Risk levels can be assessed using this technique, taking into consideration whether the generation company is either risk-taking or risk averse. The test system used in this paper comprises conventional thermal, cogeneration, and heat-only units. The pool price is considered to be uncertain while an information gap decision theory method is employed to model its volatility around the estimated value. Profits lower than the expected value are optimized using the proposed method and the related strategy is determined. The presented method optimizes the opportunities to make use of high profits or high pool prices. To verify the performance of the proposed method, the model has been implemented on a case study.

Comment on “Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-In Vehicles” by A. Y. Saber and G. K. Venayagamoorthy

This paper comments on “Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles” by [A. Y Saber and G. K. Venayagamoorthy]. There are several computational aspects in this paper that can be made more accurate with respect to the presented formulation and final results. Moreover, the conclusion of this paper contains some inaccuracies regarding plug-in vehicles. This comment first proposes an appropriate model for plug-in vehicles, whereas calculations and conclusion have been corrected, as well. Afterward, in order to improve the results, the combination of a heuristic method and particle swarm optimization (PSO) algorithm has been employed checking spinning reserve, minimum uptime and minimum downtime constraints of generating units and a de-unit commitment tool to omit the excess units. These actions with PSO result in solution improvement.

Mixed-integer Programming of Stochastic Hydro Self-scheduling Problem in Joint Energy and Reserves Markets

Abstract This article presents a stochastic model for self-scheduling of a purely hydro generating company participating in the day-ahead joint energy and reverse market aimed at maximizing the profit. In the proposed framework, the mixed-integer non-linear programming of the hydro self-scheduling problem is converted into mixed-integer programming. The hydro self-scheduling problem with uncertainties, such as outages of generation units and volatile market prices, is modeled as a stochastic mixed-integer programming problem. Random 24-hr scenarios are generated using Roulette wheel mechanism and lattice Monte Carlo simulation methods. Afterward, the proposed stochastic model is decomposed into deterministic optimization sub scenarios. For great accuracy, multi-performance curves are employed in the modeling stage. Moreover, wide-ranging parameters are taken that result in an absolutely real outcome, e.g., head dependent reservoirs, start-up cost, water discharge limits, initial and final volume, water balance constraints, spinning and non-spinning reserve, spillage of reservoir, and a cascade hydro plant. Finally, for a test system with eight hydro plants, the bilateral contracts are evaluated in terms of performance indices to demonstrate the effectiveness of proposed model.

Application of New Fast, Efficient-Self adjusting PSO-Search Algorithms in Power Systems' Studies

Abstract In this chapter, we present the validated particle swarm optimization (PSO) search and optimization technique developed by the First Author, which is based on dynamic error scaling of the weightings Omega and Delta in speed and position equations. The dynamic scaled/corrective action is based on a judicious selection of the error and its. This ensures efficient search and fast convergence to optimality condition with a reduced computational burden. The primary challenge is to find an optimal scheme to relate the error in each iteration, to the direction of particles. The primary goal is to find a scheme that moves particles toward the optimal solution. Several algorithms are presented for this purpose. The presented algorithms are compared to the conventional PSO method for multiple case studies. The algorithms are also used to solve a reactive market clearing problem. A new index is also introduced in this chapter which considers the convergence speed and the accuracy and compares the performance of the presented algorithms. Experimental results from case studies in which these proposed algorithms were tested show good performance from both an accuracy and convergence speed point of view.

Multiobjective Robust Power System Expansion Planning Considering Generation Units Retirement

This paper presents a mixed-integer linear robust multiobjective model for the expansion planning of an electric power system. An information-gap decision theory-based framework is proposed to take into account the uncertainties in electrical demand and new power system elements prices. The model is intended to increase the power system resistance against the uncertainties caused by forecast errors. The normal boundary intersection method is used to obtain the Pareto front of the multiobjective problem. Since the planning problem is a large-scale problem, the model is kept linear using the Big M linearization technique that is able to significantly decrease the computational burden. The fuel transportation and availability constraints are taken into account. The model also enables the system planner to build new fuel transportation routes whenever it is necessary. The generating units’ retirement is also incorporated into the model, and the simulation results are showed to the advantages of incorporating units’ retirement in the power system expansion planning model instead of considering it separately. The proposed multiobjective method is applied to the Garver 6-bus, IEEE 24-bus, and IEEE 118-bus test systems, and the results are compared with the well-known epsilon-constraint method.