Thermal Unit Commitment Solution using Priority List Method and Genetic-Imperialist Competitive Algorithm

A novel strategy including a Priority List (PL) based method and a heuristic algorithm which is named Genetic-Imperialist Competitive Algorithm (GICA) has been proposed in this paper to solve thermal Unit Commitment Problem (UCP). This problem has been confined by some constraints like minimum down time, minimum up time, spinning reserve, load demand, and limited output power of the generating units. The optimization process is carried out in three steps. At first, a strategy based PL is used to find units priority, in second step the GICA employed to solve Economic Load Dispatch (ELD), and finally a correction strategy tried to find and replace better solutions. The accuracy and effectiveness of the proposed method is verified by two different case studies with 4 and 10 generation units system. The comparison of results with some other methods shows that proposed three step method has a better performance and achieve better solution in an admissible time interval

Despacho económico y de unidades en Micro Redes

As a result of the differences between classical large power grids and micro grids a new approach of the Unit Commitment (UC) and Economic Dispatch (ED) problem must be proposed. The high penetrations of renewable sources and distributed energy storage systems, as well as the possibility of working in a grid-connected or island mode are some of the main issues to cope with. Firstly the advantages and drawbacks of the use of the Lambda Iteration Algorithm (LIA) for solving de ED problem in a micro grid are discussed. In order to adapt the LIA to this context some modifications have been carried out. With regard to the Unit Commitment problem, a genetic algorithm with some novel specific operators has been designed. This algorithm is suitable to deal with different constraints and scenarios arising in a micro grid environment. In addition, a comparison between the different characteristics of the designed UC algorithm and the traditional Priority List (PL) method has been performed.Producto de las diferencias existentes entre los sistemas tradicionales de generación y las micro redes (MR), el presente artículo propone un nuevo enfoque en lo que respecta la resolución de los problemas de Despacho Económico (DE) y de Unidades (DU). La fuerte presencia de energías renovables, la incorporación de sistemas de almacenamiento distribuidos y la posibilidad de que la micro red trabaje en isla o interconectada a la red principal son algunos de los aspectos a tener en cuenta a la hora de resolver dichos problemas. Primeramente se analizan las ventajas y desventajas del empleo del Algoritmo de Iteración Lambda (AIL) en la resolución de Despacho Económico, proponiéndose además modificaciones para adaptar el mismo al contexto de las micro redes. En lo que respecta a la resolución del despacho de unidades el artículo propone un algoritmo genético el cual emplea ciertos operadores que facilitan el tratamiento de las restricciones que surgen en este nuevo contexto. Finalmente se lleva a cabo una comparación entre el método de Lista de Prioridades (LP) y el algoritmo genético desarrollado.&nbsp

Operational flexibility for increasing renewable energy penetration level by modified enhanced priority list method

The increasing concerns on climate change and the need for a more sustainable grid, recently has seen a fast expansion of renewable energy sources (RES). This leads to complexities in system balancing between the load and the integrated RES generation, as a result of increased levels of system variability and uncertainty. The concept of flexibility describes the capability of the power system to maintain a balance between generation and the load under uncertainty. Therefore, system operators need to develop flexibility measuring technique to manage the sudden intermittency of net-load. Current flexibility metrics are not exhaustive enough to capture the different aspects of the flexibility requirement assessment of the power systems. Furthermore, one of their demerits is that the start-up cost is not considered together with the other technical parameters. Hence, this thesis proposes a method that improves the assessment accuracy of individual thermal units and overall generation system. Additionally, a new flexibility metric for effective planning of system operations is proposed. The proposed metric considers technoeconomic flexibility indicators possessed by generation units. A new ranking for Flexibility Ranked Enhanced Priority List (FREPL) method for increasing share of renewable energy is proposed as well. The assessment is conducted using technical and economic flexibility indicators characteristics of the generating units. An analytical hierarchy process is utilized to assign weights to these indicators in order to measure their relative significance. Next, a normalization process is executed and then followed by a linear aggregation to produce the proposed flexibility metric. Flexibility and cost ranking are coupled in order to improve the FREPL. The proposed technique has been tested using both IEEE RTS-96 test system and IEEE 10-units generating system. The developed method is integrated with the conventional unit commitment problem in order to assist the system operators for optimal use of the generation portfolios of their power system networks. The results demonstrate that the developed metric is robust and superior to the existing metrics, while the proposed Enhanced Priority List characterizes the system’s planned resources that could be operated in a sufficiently flexible manner. The net-load profile has been enhanced and the penetration level of wind power has been upgraded from 28.9% up to 37.2% while the penetration level of solar power has been upgraded from 14.5% up to 15.1%

Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference