Intelligent Energy Management for Microgrids with Renewable Energy, Storage Systems, and Electric Vehicles

The evolution of smart grid or smart microgrids represents a significant paradigm shift for future electrical power systems. Recent trends in microgrid systems include the integration of renewable energy sources (RES), energy storage systems (ESS), and plug-in electrical vehicles (PEV or EV). However, these integration trends bring with then new challenges for the design of intelligent control and management system. Traditional generation scheduling paradigms rely on the perfect prediction of future electricity supply and demand. They can no longer apply to a microgrid with intermittent renewable energy sources. To mitigate these problems, a massive and expensive energy storage can be deployed, which also need vast land area and sophisticated control and management. Electrical vehicles can be exploited as the alternative to the large and expensive storage. On the other hand, the use of electrical vehicles introduces new challenges due to their unpredictable presence in the microgrid. Furthermore, the utility and ancillary industries gradually adding sensors and power aware, intelligent functionality to home appliances for the efficient use of energy. Hence, the future smart microgrid stability and challenges are primarily dependent on the electricity consumption patterns of the home appliances, and EVs. Recently, demand side management (DSM) has emerged as a useful method to control or manipulate the user demand for balancing the generation and consumption. Unfortunately, most of the existing DSM systems solve the problem partially either using ESS to store RES energy or RES and ESS to charging and discharging of electrical vehicles. Hence, in this thesis, we propose a centralized energy management system which jointly optimizes the consumption scheduling of electrical vehicles and home appliances to reduce the peak-hour demand and use of energy produced from the RESs. In the proposed system, EVs store energy when generation is high or during off-peak periods, and release it when the demand is high compared to the generation. The centralized system, however, is an offline method and unable to produce a solution for a large-scale microgrid. Further, the real-time implementation of the centralized solution requires continuous change and adjustment of the energy generation as well as load forecast in each time slot. Thereby, we develop a game theoretic mechanism design to analyze and to get an optimal solution for the above problem. In this case, the game increases the social benefit of the whole community and conversely minimizes each household's total electricity price. Our system delivers power to each customer based on their real-time needs; it does not consider pre-planned generation, therefore the energy cost, uncertainty, and instability increase in the production plant. To address these issues, we propose a two-fold decentralized real-time demand side management (RDCDSM) which in the first phase (planning phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flat curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another repeated game with mixed strategy to mitigate the deviation between the immediate real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and energy storage systems and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator better deals with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. Now, it is envisioned that the presence of hundreds of microgrids (forms a microgrid network) in the energy system will gradually change the paradigms of century-old monopolized market into open, unbundled, and competitive market which accepts new supplier and admits marginal costs prices for the electricity. To adapt this new market scenario, we formulate a mathematical model to share power among microgrids in a microgrid network and minimize the overall cost of the electricity which involves nonlinear, nonconvex marginal costs for generation and T&D expenses and losses for transporting electricity from a seller microgrid to a buyer microgrid

Modelling and analysis of demand response implementation in the residential sector

Demand Response (DR) eliminates the need for expensive capital expenditure on the electricity distribution, transmission and the generation systems by encouraging consumers to alter their power usage through electricity pricing or incentive programs. However, modelling of DR programs for residential consumers is complicated due to the uncertain consumption behavious of consumers and the complexity of schedulling a large number of household appliances. This thesis has investigated the design and the implementation challenges of the two most commonly used DR components in the residential sector, i.e., time of use (TOU) and direct load control (DLC) programs for improving their effectiveness and implementation with innovative strategies to facilitate their acceptance by both consumers and utilities. In price-based DR programs, the TOU pricing scheme is one of the most attractive and simplest approaches for reducing peak electricity demand in the residential sector. This scheme has been adopted in many developed countries because it requires less communication infrastructure for its implementation. However, the implementation of TOU pricing in low and lower-middle income economies is less appealing, mainly due to a large number of low-income consumers, as traditional TOU pricing schemes may increase the cost of electricity for low income residential consumers and adversely affect their comfort levels. The research in this thesis proposes an alternative TOU pricing strategy for the residential sector in developing countries in order to manage peak demand problems while ensuring a low impact on consumers’ monthly energy bills and comfort levels. In this study, Bangladesh is used as an example of a lower-to-middle income developing country. The DLC program is becoming an increasingly attractive solution for utilities in developed countries due to advances in the construction of communication infrastructures as part of the smart grid concept deployment. One of the main challenges of the DLC program implementation is ensuring optimal control over a large number of different household appliances for managing both short and long intervals of voltage variation problems in distribution networks at both medium voltage (MV) and low voltage (LV) networks, while simultaneously enabling consumers to maintain their comfort levels. Another important challenge for DLC implementation is achieving a fair distribution of incentives among a large number of participating consumers. This thesis addresses these challenges by proposing a multi-layer load control algorithm which groups the household appliances based on the intervals of the voltage problems and coordinates with the reactive power from distributed generators (DGs) for the effective voltage management in MV networks. The proposed load controller takes into consideration the consumption preference of individual appliance, ensuring that the consumer’s comfort level is satisfied as well as fairly incentivising consumers based on their contributions in network voltage and power loss improvement. Another significant challenge with the existing DLC strategy as it applies to managing voltage in LV networks is that it does not take into account the network’s unbalance constraints in the load control algorithm. In LV distribution networks, voltage unbalance is prevalent and is one of the main power quality problems of concern. Unequal DR activation among the phases may cause excessive voltage unbalance in the network. In this thesis, a new load control algorithm is developed with the coordination of secondary on-load tap changer (OLTC) transformer for effective management of both voltage magnitude and unbalance in the LV networks. The proposed load control algorithm minimises the disturbance to consumers’ comfort levels by prioritising their consumption preferences. It motivates consumers to participate in DR program by providing flexibility to bid their participation prices dynamically in each DR event. The proposed DR programs are applicable for both developed and developing countries based on their available communication infrastructure for DR implementation. The main benefits of the proposed DR programs can be shared between consumers and their utilities. Consumers have flexibility in being able to prioritise their comfort levels and bid for their participation prices or receive fair incentives, while utilities effectively manage their network peak demand and power quality problems with minimum compensation costs. As a whole, consumers get the opportunity to minimise their electricity bills while utilities are able to defer or avoid the high cost of their investment in network reinforcements

Óptima ubicación y dimensionamiento de bancos de capacitores usando compensación Volt-Var en micro-redes eléctricas.

In this work, a mathematical model is implemented to solve optimally the location and sizing of capacitor banks in a micro-grid of electrical distribution. The proposed method is by particle swarm optimization (PSO), the solution will be obtained with dynamic programming, implementing an algorithm that will be developed entirely in MATLAB. Finding an optimal location and reactive power capacity of the capacitor banks, by means of reactive power injection in one or several established bars. The algorithm calculates the optimal solution satisfying the necessary requirements to guarantee the reliability and reliability of the system, as a case study a model of a typical micro-network of 5 bars will be used, however, the algorithm was implemented in a general way and can respond correctly before systems with multiple bars. As a final analysis, we made a comparison of the uncompensated system data and the compensated system data, highlighting an improvement in voltage profiles, minimizing line losses, minimizing total system losses, improving the power factor, decreasing the maximum voltage deviation and average voltage deviation, complying with a minimum possible cost restriction criterion.En este trabajo, se implementó un modelo matemático para resolver de forma óptima la ubicación y dimensionamiento de bancos de capacitores en una micro-red de distribución eléctrica. El método propuesto es mediante Optimización por Enjambre de Partículas (PSO), la solución se obtendrá con programación dinámica, implementando un algoritmo que será desarrollado íntegramente en MATLAB. Encontrando una óptima ubicación y capacidad de potencia reactiva de los bancos de capacitores, por medio de inyección de potencia reactiva en una o varias barras establecidas. El algoritmo calcula la solución óptima satisfaciendo los requerimientos necesarios para garantizar la fiabilidad y confiabilidad del sistema, como caso de estudio se utilizará un modelo de una micro-red típica de 5 barras, sin embargo, el algoritmo se implementó de manera general y puede responder correctamente ante sistemas con múltiples barras. Como análisis final realizamos una comparación de los datos del sistema sin compensar y los datos del sistema compensados, resaltando una mejoría en los perfiles de voltaje, minimizando las pérdidas en las líneas, minimizando las pérdidas totales del sistema, mejorando el factor de potencia, disminuyendo la desviación máxima de voltaje y desviación promedio de voltaje, cumpliendo con un criterio de restricción de mínimo costo posible

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Advanced Optimization and Data-Driven Control in Smart Grid

The power grids are continuously evolving over the past decades, where new challenges and opportunities are embraced at the same time. On one hand, the penetration of renewable generations and other distributed energy resources (DER) is growing rapidly, whose different generation and control patterns could significantly impact the daily operation. On the other hand, the new communication, monitoring and regulating devices are gradually installed, which enable more control abilities of the generations, demands, and grids, and the feasibility to deploy more sophisticated control schemes.To leverage the new technique and overcome the new challenges in the smart girds, different optimization and control problems need to be solved for different roles including the system operator, demand, and financial traders. For the system operators, it is critical to maximizing the total social welfare while satisfying the operational constraints. To better coordinate the DER and improve the efficiency of distribution systems, the three-phase optimal power flow (OPF) problem algorithms are developed including the DCOPF algorithm for robustness and the ACOPF algorithm for optimality. Moreover, the deep reinforcement learning-based Volt-VAR control schemes are proposed to better maintain the voltage stability and electricity service quality.For demands resources, minimizing their energy bills will satisfy the energy needs is always their goal. Providing ancillary services by proactively adjusting their total demand is one of the potential choices. Through the provision of the services, the demands can not only receiving incentives from the system operators but also help to improve the reliability and stability of power grids. We develop control schemes specifically for the data centers to provide the phase balancing service in the distribution system and the frequency regulation service in the transmission system. The financial traders, it is desired to maximize their total profits. A better trading strategy with a more accurate forecast model can help increase the traders' gain and further improve the price convergence of the electricity market. Our machine learning based trading framework outperforms the existing approach and lays the foundation for market efficiency evaluation across the markets

Compensación VOLT-VAR mediante despacho óptimo de generación en microrredes eléctricas con alta penetración de generación distribuida

En el presente trabajo se desarrolló un modelo de compensación de potencia activa y reactiva, en una microrred eléctrica a la que se implementó un flujo óptimo de potencia, para un análisis preciso del flujo que circula por la microrred, favoreciendo la integración de fuentes de generación de origen renovable. El método propuesto es por programación lineal entera mixta (MILP), al que se incorporó generación distribuida (GD) en una o varias barras establecidas. El algoritmo calcula una solución óptima, que satisface los requerimientos necesarios para garantizar fiabilidad y calidad en la microrred; desarrollado en la herramienta GAMS minimizando costos operativos en el sistema como función objetivo. Como análisis final se realizó una comparación de datos del sistema sin GD y con GD destacando una mejoría de los perfiles de voltaje, reducción de las pérdidas de potencia activa en las líneas, una mejora en el factor de potencia y comportamiento angular; que cumple con las restricciones planteadas a un mínimo costo de operación. Para el modelamiento del problema se utilizó el sistema de 9 barras de la IEEE.In the present work, an active and reactive power compensation model was developed, in an electric microgrid to which an optimal power flow was implemented, for a precise analysis of the flow circulating through the microgrid, favoring the integration of generation sources of renewable origin. The proposed method is by mixed integer linear programming (MILP), to which distributed generation (GD) was incorporated in one or more established bars. The algorithm calculates an optimal solution, which satisfies the necessary requirements to guarantee reliability and quality in the microgrid; developed in the GAMS tool minimizing operating costs in the system as an objective function. As a final analysis, a comparison of data from the system without GD and with GD was carried out, highlighting an improvement in the voltage profiles, reduction of the losses of active power in the lines, an improvement in the power factor and angular behavior; that complies with the restrictions set forth at a minimum operating cost. To model the problem, the IEEE 9-bar system was used