Energy management strategies based on fuzzy logic control for grid-tied domestic electro-thermal microgrid

The environmental and economic benefits related to the reduction of both carbon dioxide emission and transmission losses have made distributed renewable generation systems became a competitive solution for future power systems. In this context, Microgrids (MG) are considered as the key building blocks of smart grids and have aroused great attention in the last decade for their potential and the impact they may have in the coming future. The MG concept has captured great attention in the last years since it can be considered one of the most suitable alternatives for integration of distributed generation units in the utility grid. However, this integration involves some challenges to deal with especially when penetration of Renewable Energy Sources (RES) into the distribution network is increased. Therefore, an effective Energy Management System (EMS) is required to ensure optimal energy utilization within the MG, consequently, facilitating both the grid integration and operator control. In this regard, the EMS strategy design depends on the application, MG power architecture, and the power management capability of the MG elements. This dissertation research focuses on the design of different EMS strategies based on Fuzzy Logic Control (FLC) for a residential grid-connected electro-thermal MG including renewable power generation (i.e. photovoltaic and wind turbine generators) and storage capability (i.e. battery bank and water storage tank). The main goal of the FLC-based EMS strategies is to minimize the grid power fluctuations while keeping the battery State-of-Charge (SOC) within secure limits. In order to accomplish this goal, the controller design parameters, such as membership functions and rule-base, of the FLC-based EMS strategies, are adjusted to optimize a pre-defined set of quality criteria of the MG behavior. The analysis and design of the FLC-based EMS strategies for electrical and electro-thermal MG power architectures are developed considering two different scenarios. A first scenario where the MG power forecasting is not provided and a second scenario where the forecast of generation power and load demand are considered. A comparison with the different EMS strategies is presented in simulation level, whereas the features of the enhanced FLC-based EMS strategies are experimentally tested on a real residential microgrid implemented at the Public University of Navarre (UPNa)Este estudio presenta el diseño de diferentes estrategias de gestión energética basadas en un controlador difuso para una microrred electro-térmica residencial conectada a la red eléctrica compuesta por generadores de energía renovable (solar y eólico) y elementos de almacenamiento de energía (banco de baterías y tanque de almacenamiento de agua). El objetivo principal de las estrategias de gestión es reducir los picos y fluctuaciones de potencia en el perfil de potencia intercambiado con la red eléctrica y preservar la vida útil del sistema de almacenamiento. Se presenta una revisión del estado del arte de estudios anteriores que buscan este objetivo. Se muestra el análisis de dos arquitecturas de microrred. La primera arquitectura consiste en una microrred eléctrica compuesta fuentes de energía renovables, sistema de almacenamiento de energía y el consumo eléctrico de una vivienda. La segunda arquitectura consiste en una microrred electro-térmica que contiene los elementos de la microrred eléctrica e incluye adicionalmente generadores térmicos y el consumo térmico de la vivienda. Con el objetivo de medir la eficiencia de las diferentes estrategias de gestión, se presenta un conjunto de criterios de evaluación que analizan la calidad del perfil de potencia intercambiado con la red eléctrica obtenido mediante las diferentes estrategias de gestión energética. Estos criterios de calidad son utilizados adicionalmente para la optimización de parámetros de los controladores difusos, lo cual se realiza mediante un proceso de aprendizaje fuera de línea que considera los datos históricos del comportamiento de la microrred. La comparación entre las diferentes estrategias de gestión energética se realiza mediante simulación, utilizando los datos reales de generación y consumo adquiridos en la Universidad Pública de Navarra durante el período comprendido entre Julio 2013 y Julio 2014. El diseño de las estrategias de gestión energética para la arquitectura de microrred eléctrica supone dos posibles escenarios, el primer escenario no considera la previsión de consumo y generación de la microrred, y el segundo escenario si considera esta previsión. Las prestaciones de las estrategias basadas en control difuso para cada uno de estos escenarios son validadas experimentalmente en condiciones reales en la microrred de la Universidad Pública de Navarra. Finalmente, se presenta el análisis de las estrategias de gestión basadas en control difuso empleadas a la arquitectura de microrred electro-térmica. La comparación, mediante simulación, con otras estrategias de gestión aplicadas a la misma arquitectura ha demostrado el correcto desempeño de las estrategias desarrolladas basadas en control difuso.Postprint (published version

Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting

This paper presents the design of an energy management strategy based on a low complexity Fuzzy Logic Control (FLC) for grid power profile smoothing of a residential grid-connected microgrid including Renewable Energy Sources (RES) and battery Energy Storage System (ESS). The proposed energy management strategy uses generation and demand forecasting to anticipate the future behavior of the microgrid. Accordingly to the microgrid power forecast error and the Battery State-of-Charge (SOC) the proposed strategy performs the suitable control of the grid power. A simulation comparison with previous energy management strategies highlights the advantages of the proposed work minimizing fluctuations and power peaks in the power profile exchanged with the grid while keeping the energy stored in the battery between secure limits. Finally, the experimental validation in a real residential microgrid implemented at Public University of Navarre (UPNa, Spain) demonstrates the proper operation of the proposed strategy achieving a smooth grid power profile and a battery SOC center close to the 75% of the rated battery capacity.Peer ReviewedPostprint (author's final draft

A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions

It is expected, and regionally observed, that energy demand will soon be covered by a widespread deployment of renewable energy sources. However, the weather and climate driven energy sources are characterized by a significant spatial and temporal variability. One of the commonly mentioned solutions to overcome the mismatch between demand and supply provided by renewable generation is a hybridization of two or more energy sources in a single power station (like wind-solar, solar-hydro or solar-wind-hydro). The operation of hybrid energy sources is based on the complementary nature of renewable sources. Considering the growing importance of such systems and increasing number of research activities in this area this paper presents a comprehensive review of studies which investigated, analyzed, quantified and utilized the effect of temporal, spatial and spatio-temporal complementarity between renewable energy sources. The review starts with a brief overview of available research papers, formulates detailed definition of major concepts, summarizes current research directions and ends with prospective future research activities. The review provides a chronological and spatial information with regard to the studies on the complementarity concept.Comment: 34 pages 7 figures 3 table

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

: Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resilienc

Flexible active power control for PV‐ESS systems:A review

The penetration of solar energy in the modern power system is still increasing with a fast growth rate after long development due to reduced environmental impact and ever-decreasing photovoltaic panel cost. Meanwhile, distribution networks have to deal with a huge amount and frequent fluctuations of power due to the intermittent nature of solar energy, which influences the grid stability and could cause a voltage rise in the low-voltage grid. In order to reduce these fluctuations and ensure a stable and reliable power supply, energy storage systems are introduced, as they can absorb or release energy on demand, which provides more control flexibility for PV systems. At present, storage technologies are still under development and integrated in renewable applications, especially in smart grids, where lowering the cost and enhancing the reliability are the main tasks. This study reviews and discusses several active power control strategies for hybrid PV and energy storage systems that deliver ancillary services for grid support. The technological advancements and developments of energy storage systems in grid-tied PV applications are also reviewed

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

Why Do Electricity Policy and Competitive Markets Fail to Use Advanced PV Systems to Improve Distribution Power Quality?

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the network characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. We discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control

Optimal coordination of energy sources for microgrid incorporating concepts of locational marginal pricing and energy storage

This research aims to coordinate energy sources for standalone microgrid (MG), incorporating locational marginal pricing (LMP) and energy storage. Two approaches are suggested for the optimal energy management of MG. First, the energy management of a standalone MG is performed utilising the concept of LMP. The objective is to minimise the average LMP to reduce network congestion and power loss costs. Second, energy management is performed using a dual-stage energy management approach. A BESS model is formulated considering charging and discharging characteristics and utilised in this research for dual-stage energy management. The impact of the battery state of charge (SOC) is assessed in the optimal day-ahead operation. An incremental cost factor is included with battery SOC when calculating the system operating cost. A new binary jellyfish search algorithm (BJSA) is developed to solve energy management problems. The suggested BJSA technique is implemented in solving the optimal energy management of MG considering LMP. The simulations of the suggested approach are conducted on the IEEE 14 and 30-bus test systems. Results show that the BJSA technique is more consistent than the binary particle swarm optimisation (BPSO) technique in determining the optimal solution. In addition, the BJSA technique is employed to solve the dual-stage energy management of MG considering BESS. The proposed approach is simulated on the IEEE 14 and 30-bus systems. Results also show that the BJSA technique is superior to the BPSO technique in minimising the operating cost in real-time economic dispatch (ED). The performance of the BJSA and BPSO techniques is exactly similar to the UC schedule with and without BESS considering the IEEE 30-bus system, like the IEEE 14-bus system. The BJSA technique minimises operating costs by up to 5% over the BPSO technique for the UC schedule with power loss. Operating costs are reduced by up to 5% using the BJSA technique rather than the BPSO technique for real-time ED with BESS. However, the BPSO technique is inconsistent and fails to obtain the same results for the IEEE 30-bus system. Overall, the findings confirm the superiority of the suggested BJSA technique and the suggested optimisation approaches in optimising the energy management of MG