Shantanu Chakraborty

Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system

Optimal cooperative microgrid formation strategy for minimizing distribution loss (26-80% reduction).The microgrid coalition game is designed and analyzed by Coalitional Game Theory.Scalable for a large number of microgrids and computationally efficient.Applicable to (near) real-time operation due to lower computational and communicative complexity. This paper presents an optimal coalition formation mechanism of microgrids in a smart distribution system and analyzes the characteristics from the coalitional game theoretical perspective. Microgrids coalitions can (1) minimize the energy burden and dependency on the utility grid, (2) minimize the overall grid network power loss, and (3) maximize intra-coalition energy transfer. In order to form cooperative microgrids, a Hierarchical priority based Coalition Scheme (HRCoalition) is proposed. Given an intra-coalition distance threshold, the proposed HRCoalition mechanism can provide the optimal coalition that achieves the aforementioned objectives. The optimality is realized by reaching a state of cooperative equilibrium for all microgrids and coalitions. The optimality of the formed coalitions is proved by Coalitional Game Theory. A Greedy based strategy is designed to perform network constrained energy exchange (GreedEnEx) within a formed coalition. Thus, HRCoalition provides a higher level optimization while, GreedEnEx yields system level optimization using output of HRCoalition. The proposed HRCoalition scheme is computationally very efficient and can scale up to a huge number of microgrids and thus makes it suitable for near real-time operation. An equivalent pricing mechanism is designed to provide a form of economic incentive to the microgrids participating coalition formation. The performance of the proposed method is reported to scale up to 500 microgrids with a loss reduction ranging from 26% to 80%. The provided numerical simulation results back the claim of optimality as well as prove the effectiveness of the proposed coalition formation method.

Thermal unit commitment strategy with solar and wind energy systems using genetic algorithm operated particle swarm optimization

This paper presents a methodology for solving unit commitment problem for thermal units integrated with wind and solar energy systems. The renewable energy sources are included in this model due to their low electricity cost and positive effect on environment. The unit commitment problem is solved by a genetic algorithm operated improved binary particle swarm optimization (PSO) algorithm. Unlike trivial PSO, this algorithm runs the refinement process of the solutions within multiple populations. Some genetic algorithm operators such as crossover, elitism, mutation are applied within the higher potential solutions to generate new solutions for next population. The PSO includes a new variable for updating velocity in accordance with population best with particle best and global best. The algorithm performs effectively in various sized thermal power system with equivalent solar and wind energy system and is able to produce high quality (minimized production cost) solutions. The simulation results show the effectiveness of this algorithm by comparing the outcome with several established methods.

Fuzzy controller based output power leveling enhancement for a permanent magnet synchronous generator

Due to irregular wind velocity, the output power of a wind turbine generator system (WTGS) is fluctuated. There are many methods to propose to generate smooth output power of a wind turbine. For example, energy storage devices, electric double layer capacitors, flywheels are well-known. But these methods are required a significant extra cost for installation and maintenance. In recent years, some researches have been conducted to generate smooth output power by using inertia or by controlling kinetic energy of a wind turbine. The major benefit of this method, it does not require extra energy storage to generate smooth output power. So, it can reduce of a system cost significantly. But this method is reduced output power radically at the steady wind velocity as compare with maximum power point tracking (MPPT) control method. To overcome this problem, this paper is proposed a fuzzy controller based output power smoothing method by controlling kinetic energy of a wind turbine. The generator electrical speed is controlled by the proposed method that helps to generate efficient smooth output power at different wind speeds. The proposed method is compared with conventional method and MPPT control method. The effectiveness of the proposed method is verified by MATLAB SIMULINK with SimPowerSystems and Fuzzy Logic Toolbox.

A new method for next-day price forecasting for PJM electricity market

In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.

Next-day electricity price forecasting on deregulated power market

This paper proposes the approach to reduce the prediction error at occurrence time of peak electricity price, and aims to enhance the accuracy of next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the NN at occurrence time of peak electricity price in order to catch the price variation. Moreover, learning data for the neural network (NN) is selected by rough sets theory at occurrence time of peak electricity price. This method is examined by using the data of PJM electricity market.

Generation Scheduling of Thermal Units Integrated with Wind-Battery System Using a Fuzzy Modified Differential Evolution Approach

This paper presents a fuzzy methodology for solving thermal unit commitment problem integrated with wind power system using differential evolution approach. Wind power facility is coupled with an equivalent battery to compensate with fre- quency and voltage fluctuations. Wind energy system is integrated with the system due to lower electric cost and positive effect on the environment. But due to the uncertainty of wind speed and hence wind power generation and load forecasting, a crisp opti- mization method may fail short providing the effective solution. Therefore, to solve the problem effectively, this model handles such imprecision by fuzzyfication. Then the unit commitment problem is solved by using a modified differential evolution approach. Trivial differential evolution method is modified to work with thermal scheduling problem which is a mixed-integer problem requiring discrete optimization. Several simulations are presented in order to demonstrate the effectiveness of the proposed method.

Application of incentive based scoring rule deciding pricing for smart houses

This paper presents a smart pricing scheme for smart house facilitating a scoring rule operated reward based pricing mechanism. In this scheme, provider (EP) monitors the network load and propose a day-ahead pricing to the consumers. The consumers respond that pricing by providing a probabilistic device schedule prediction for the smart devices to the lower period. The EP incentivizes the consumers by offering rewards (or discount) over the price depending on the accuracy margin of truthfulness of shifting-period forecast. Such reward function is formed based on a Continuous Ranked Probability Score (CRPS). CRPS has the ability to be a strictly proper scoring rule as well as to assess the closeness of estimation. Finally an optimization problem is formed which reduces EPs costs of providing rewards and satisfying consumers demand. The simulation results will show the effectiveness of the proposed method.

Fuzzy quantum computation based thermal unit commitment strategy with solar-battery system injection

This article presents a strategy to solve thermal unit commitment (UC) integrated with an equivalent solar-battery system using Fuzzy based Quantum inspired Evolutionary Algorithm (FQEA). As a renewable power source, solar power is injected stochastically with the model. To handle the uncertainty and intermittency involved while integrating solar power and load forecasting, the trivial crisp problem formulations are modified by fuzzification. An evolutionary algorithm based on the concept and principle of quantum computation is applied to solve the UC problem. The conventional Quantum Evolutionary Algorithm (QEA) is advanced by using several operators such as binary differential operator, mutation and crossover along with trivial rotation operator with a re-defined rotational angle look-up table. The QEA is further modified by introducing multi-population based scheme. The fitness function is formulated by combining the objective function, penalty function and the aggregated fuzzy membership function. The proposed FQEA is applied to UC problem in different scaled power systems up to 100 units. Provided simulation results will show the effectiveness of FQEA.

Security constrained unit commitment strategy for wind/thermal units using Lagrangian relaxation based Particle Swarm Optimization

System security in the generation market is one of the important aspects in power system operation under deregulated environment. It becomes more crucial when thermal power system is integrated with wind system. This paper presents an approach to determine the security constrained unit commitment (SCUC) for thermal units integrated with wind power system. A Lagrangian relaxation based algorithm with Particle Swarm Optimization (PSO) has been applied to solve this model. The method initially decomposes the load demand hours into several groups based on their homogeneity. Then instead of solving hourly SCUC, this method solves SCUC for each group. Lagrangian formulations are applied to relax the constraints with objective function using multipliers. PSO is then applied to solve UC. Since security constraints including transmission flow and voltage limits are considered, an iterative sub problem is introduced to minimize the security constraint violation using a simplified heuristic method. The Lagraingian multipliers are updated using gradient method. The process will continue until the difference between the primal and dual problem comes to a tolerable limit. To compromise the uncertainty of wind power, it is injected with the provided supply using a Gaussian distribution stochastic function. The simulation provides some analysis of the proposed method with two test systems (IEEE 6-bus and and 31-bus).

Fuzzy unit commitment strategy integrated with solar energy system using a modified differential evolution approach

This paper presents a fuzzy methodology for solving thermal unit commitment problem integrated with solar energy system using differential evolution approach. Solar energy with battery is integrated with the model due to lower electric cost and positive effect on the environment. Such inclusion of intermittent solar energy with thermal power generators, requires sophisticated methodology since the uncertainty is involved. Therefore the solar radiation, forecasted load demand and associated constraints are formulated as fuzzy sets considering the error. Then the unit commitment problem is solved using a modified differential evolution approach. Several simulations are presented in order to demonstrate the effectiveness of the proposed method.