Prerna Jain

Wind Power Scenario Generation and Reduction in Stochastic Programming Framework

Abstract Wind power trading in pool-based electricity markets is a decision-making problem and is generally modeled using a multi-stage stochastic programming approach because of the implicit uncertainty of wind input. In any stochastic programming approach, representation of random input process is a major issue. Due to uncertainty in wind availability, generated power by wind turbines is stochastic and is represented by possible values with corresponding probability of occurrence or scenarios. Accurate representation of uncertainty generally requires the consideration of large number of scenarios, thus necessitating the need for scenario-reduction techniques. This article presents simplified algorithms for wind power scenario generation and reduction. A time series based auto regressive moving average model is used for scenario generation, and probability distance based backward reduction is used for scenario reduction. The algorithms have been implemented for next-day scenario generation of wind farm located at Barnstable, Massachusetts, USA. The results prove the ability of the proposed algorithms in wind uncertainty modeling. These algorithms can successfully be utilized to generate optimal wind power bids for trading in electricity markets.

A new hybrid technique for solution of economic load dispatch problems based on Biogeography Based Optimization

This paper presents a hybrid technique combining Evolutionary Strategy(ES) with Biogeography Based Optimization (BBO/ES) algorithm to solve economic load dispatch problems of thermal plants considering equality and inequality constraints, transmission losses and valve point loading. Biogeography is a recently developed heuristic algorithm which has shown impressive performance on many well known benchmarks. In order to improve BBO, distinctive features from ES are incorporated in BBO modification. This algorithm searches for the global optimum mainly through two steps: migration and mutation. The effectiveness of the proposed algorithm has been verified on two different test systems, both small and large involving varying degree of complexity. A comparison of simulation results reveals that the proposed algorithm is better than those listed in the comparison table in terms of the quality of the solution. This method seems to be promising alternative approach for solving the ELD problems in practical power systems.

Profit maximization of a generation company based on Biogeography based Optimization

In a deregulated electricity market, generating companies aim to maximize their profit, by bidding optimally in the day-ahead market, under incomplete information of the competing generators. This paper develops an optimal bidding strategy for a thermal generator, considering a nonlinear operating cost function. Each generating company offers block bid as price and quantity pairs and sealed auction with a pay-as-bid is employed. Rival bidding behavior is described using normal probability distribution function, and the optimal bidding strategy for a generation company is formulated as a stochastic optimization problem. This is solved using Monte Carlo Simulations with Biogeography based Optimization (BBO) approach. BBO is a new heuristic algorithm that retains the properties of all good solutions and improves the quality of poor solutions, in the entire population of feasible solutions. The effectiveness of the proposed method is tested on a sample system, and optimal bid quantities and prices are obtained.

Blended Biogeography Based Optimization for different economic load dispatch problem

Blended Biogeography Based Optimization (B-BBO) is an extension to the Biogeography Based Optimization (BBO) technique. Here the blending operator is used in the migration operation of BBO, inspired by the blended crossover operator in Genetic Algorithm (GA). In this paper, the B-BBO technique is applied on economic load dispatch (ELD) problems. Two sample systems are taken with quadratic and valve point cost functions respectively to analyze the effect of blending for the solution of this problem using BBO. Our analysis concludes that computational time decreases effectively with the blending but objective function value increases. B-BBO is suitable to the cases where huge computational burden is a big problem.

Influence of Bidding Mechanism and Spot Market Characteristics on Market Power of a Large Genco Using Hybrid DE/BBO

AbstractGeneration company (Genco) bidding in an electricity market (EM) aims to maximize its profit under uncertain market characteristics and a regulated bidding mechanism. This paper addresses the strategic bidding for a large price maker Genco and empirically investigates the effect of a step-wise multiple segment bidding mechanism and EM characteristics, such as demand and rivals’ behavior, on its market power (MP) potential and efficiency. The methodology of using novel hybrid differential evolution with biogeography-based optimization (DE/BBO), employing the sinusoidal migration model, is proposed for strategic bidding. DE exploration with BBO exploitation enhances global optimization. Uncertain rival behavior is modeled as normal distribution and simulated by the Monte Carlo technique. The proposed approach is validated for large Genco bidding in spot EM, under changing market characteristics and bidding segments. The implicit MP potential and efficiency of Genco for corresponding strategies is as...

Biogeography based optimization for different economic load dispatch problem with different migration models

This paper is an extension made in biogeography based optimization (BBO) by modifying its migration models to make it more realistic. This BBO technique is then applied on simple economic load dispatch (ELD) problem and ELD with valve point effect to analyze the effect of different migration models. Our analysis concludes that the quadratic and sinusoidal models provide the best result for the ELD and ELD valve point effect problem respectively.

An optimally controlled charging scheme motivating EV owners for supporting grid stability

Electric Vehicles (EVs) significantly support green transportation while alleviating pressure of oil export and carbon emissions. Abrupt and uncontrolled charging of these active storage units may increase peak loading and cause complications like need for reinforcement of distribution grid to maintain stability. Simultaneous charging scenario, active mostly at peak hours, also increases the charging cost burden on EV owners. To maximize benefits of using EVs, regulated and optimized charging control needs to be provided for vehicles by Load Aggregators (LAs) with the perspective of minimizing charging cost. LA being a commercial entity aims at revenue maximization. Considering this, an optimal EV charging scheduling problem for a LA is developed by considering its revenue and EV owners demands and costs. Adaptive charging rate or power is proposed for each vehicle to attain grid stability. Static charging scenario is considered in which vehicle movement patterns are known to the LA in advance. The Linear Programming Problem is solved using simplex method in MATLAB. Simulation results based on real electricity price and load data are presented to show the revenue gains and cost savings by optimal charging scheduling. Comparative analysis of LAs revenue and charging costs is carried out for uncontrolled and controlled charging scenarios. Additionally, their sensitivity towards number of vehicles and upper charging rate limit is analyzed. Results show interesting trends regarding the economics of EV charging, grid stability and charging rate control.

Coordinated GEP and TEP integrating correlated solar generation and load

Considering the uncertainties associated with the daily profiles of solar power generation and loads, this paper offers modelling to reflect their correlation. Probability distributions are modelled for all uncertain parameters for each hour and respective hourly scenarios are generated by Monte Carlo simulation. These scenarios are then integrated in Coordinated Generation Expansion Planning (GEP) and Transmission Expansion Planning (TEP) framework which offers optimal solution for system expansion. A comparative study reflects the difference in decisions making, with varying load profile.

Security-Constrained Unit Commitment for Joint Energy and Reserve Market Based on MIP Method

In this work, a Security-Constrained Unit Commitment (SCUC) is proposed for day-ahead scheduling with joint energy and reserve markets. Independent System Operator (ISO) executes SCUC to optimize reserve requirements in restructured power system. Though, SCUC structure determines reserve requirements for simulating UC necessities and line contingency with DC optimal power flow (DCOPF) for adequate purpose of energy market. In this context, proposed SCUC problem is formulated using Mixed Integer Programming (MIP) in which schedule and dispatch of generating units is considered to be deterministic. The overall objective of this paper, ISO is to minimize the cost of supply energy and reserve requirement over the optimization horizon (24 h) subject to satisfying all the operating and network security constraints. However, comprehensive Benders decomposition (BD) is new to explain SCUC formulation and simulation results are compare without and by inclusion of network security. In order to show that effective reach of the proposed model, it is executed on a transmission system of modified IEEE-30 bus system with seven generating units on Zone A and two generating units on Zone B. Moreover various, test cases are investigated and compared, which shows that the proposed optimization model is promising.

Security Constrained Unit Commitment in a Power System based on Benders Decomposition and Mixed Integer Non-linear Programming

In deregulated power markets, Independent System Operators (ISOs) maintains adequate reserve requirement in order to respond to generation and system security constraints. In order to estimate accurate reserve requirement and handling non-linearity and non-convexity of the problem, an efficient computational framework is required. In addition, ISO executes SCUC in order to reach the consistent operation. In this paper, a novel type of application which is Benders decomposition (BD) and Mixed integer non linear programming (MINLP) can be used to assess network security constraints by using AC optimal power flow (ACOPF) in a power system. It performs ACOPF in network security check evaluation with line outage contingency. The process of solving modified system would be close to optimal solution, the gap between the close to optimal and optimal solution is expected to determine whether a close to optimal solutionis accepetable for convenientpurpose. This approach drastically betters the fast computational requirement in practical power system .The numerical case studies are investigated in detail using an IEEE 118-bus system.