Design of an Incentive-based Demand Side Management Strategy for Stand-Alone Microgrids Planning

Demand Side Management Strategies (DSMSs) can play a significant role in reducing installation and operational costs, Levelized Cost of Energy (LCOE), and enhance renewable energy utilization in Stand-Alone Microgrids (SAMGs). Despite this, there is a paucity in literature exploring how DSMS affects the planning of SAMGs. This paper presents a methodology to design an incentive-based DSMS and evaluate its impact on the planning phase of a SAMG. The DSMS offers two kinds of incentives, a discount in the flat tariff to increase the electrical energy consumption of the users, and an extra payment added to the fare to penalize it. The design of the methodology integrates the optimal energy dispatch of the energy sources, the tariff design, and its sizing. In this regard, the main contribution of this paper is the design of an incentive-based DSMS using a Disciplined Convex approach, and the evaluation of its potential impacts over the planning of SAMG. The methodology also computes how the profits of the investors are modified when the economic incentives vary. A study case shows that the designed DSMS effectively reduces the size of the energy sources, the LCOE, and the payments of the customers for the purchased energy

An MILP approach for the optimal design of renewable battery-hydrogen energy systems for off-grid insular communities

Abstract The optimal sizing of stand-alone renewable H2-based microgrids requires the load demand to be reliably satisfied by means of local renewable energy supported by a hybrid battery/hydrogen storage unit, while minimizing the system costs. However, this task is challenging because of the high number of components that have to be installed and operated. In this work, an MILP optimization framework has been developed and applied to the off-grid village of Ginostra (on the Stromboli island, Italy), which is a good example of several other insular sites throughout the Mediterranean area. A year-long time horizon was considered to model the seasonal storage, which is necessary for off-grid areas that wish to achieve energy independence by relying on local renewable sources. The degradation costs of batteries and H2-based devices were included in the objective function of the optimization problem, i.e., the annual cost of the system. Efficiency and investment cost curves were considered for the electrolyzer and fuel cell components in order to obtain a more detailed and precise techno-economic estimation. The design optimization was also performed with the inclusion of a general demand response program (DRP) to assess its impact on the sizing results. Moreover, the effectiveness of the proposed MILP-based method was tested by comparing it with a more traditional approach, based on a metaheuristic algorithm for the optimal sizing complemented with ruled-based strategies for the system operation. Thanks to its longer-term storage capability, hydrogen is required for the optimal system configuration in order to reach energy self-sufficiency. Finally, considering the possibility of load deferral, the electricity generation cost can be reduced to an extent that depends on the amount of load that is allowed to participate in the DRP scheme. This cost reduction is mainly due to the decreased capacity of the battery storage system

A review of constraints and adjustable parameters in microgrids for cost and carbon dioxide emission reduction

In a world grappling with escalating energy demand and pressing environmental concerns, microgrids have risen as a promising solution to bolster energy efficiency, alleviate costs, and mitigate carbon emissions. This article delves into the dynamic realm of microgrids, emphasizing their indispensable role in addressing today's energy needs while navigating the hazards of pollution. Microgrid operations are intricately shaped by a web of constraints, categorized into two essential domains: those inherent to the microgrid itself and those dictated by the external environment. These constraints, stemming from component limitations, environmental factors, and grid connections, exert substantial influence over the microgrid's operational capabilities. Of particular significance is the three-tiered control framework, encompassing primary, secondary, and energy management controls. This framework guarantees the microgrid's optimal function, regulating power quality, frequency, and voltage within predefined parameters. Central to these operations is the energy management control, the third tier, which warrants in-depth exploration. This facet unveils the art of fine-tuning parameters within the microgrid's components, seamlessly connecting them with their surroundings to streamline energy flow and safeguard uninterrupted operation. In essence, this article scrutinizes the intricate interplay between microgrid constraints and energy management parameters, illuminating how the nuanced adjustment of these parameters is instrumental in achieving the dual objectives of cost reduction and Carbon Dioxide emission minimization, thereby shaping a more sustainable and eco-conscious energy landscape. This study investigates microgrid dynamics, focusing on the nuanced interplay between constraints and energy management for cost reduction and Carbon Dioxide minimization. We employ a three-tiered control framework—primary, secondary, and energy management controls—to regulate microgrid function, exploring fine-tuned parameter adjustments for optimal performance

Fuzzy logic controller design for battery management in a grid connected microgrid

Projecte realitzat en col•laboració amb el Departamento de Eléctrica y Electrónica de la Universidad Pública de Navarra.[ANGLÈS] Starting from a previous study made by Universidad Pública de Navarra, in which the battery energy management of an electric micro grid is achieved using a control system built in two steps, a fuzzy logic design is proposed to improve the performance of above. This improvement is evaluated by simulations analyzing the power profile injected to the grid and the battery state of charge. In addition the performance of a thermoelectric micro grid is analyzed to improve the power profile injected to the grid by using a fuzzy logic controller for its energy management.[CASTELLÀ] A partir de un estudio previo realizado por la Universidad Pública de Navarra, en el cual se realizó la gestión energética de la batería de una microrred eléctrica mediante el desarrollo de un sistema de control construido en dos fases, se propone el diseño de un controlador borroso que mejore las prestaciones del anterior. Esta mejora se evalúa mediante simulación en términos del perfil de potencia inyectado a la red eléctrica y el estado de carga resultante de la batería. Además se analiza el rendimiento de una microrred electrotérmica en función del perfil de red obtenido mediante un controlador borroso

Integration of electric vehicles in a flexible electricity demand side management framework

Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of electric vehicle ownership rapidly increasing, uncontrolled charging of large populations may bring about power system issues such as increased peak demand and voltage variations, while at the same time the cost of electricity generation, as well as the resulting Greenhouse Gases (GHG) emissions, will also rise. The work reported in this PhD Thesis aims to provide solutions to the three significant challenges related to EV integration, namely voltage regulation, generation cost minimisation and GHG emissions reduction. A novel, high-resolution, bottom-up probabilistic EV charging demand model was developed, that uses data from the UK Time Use Survey and the National Travel Survey to synthesise realistic EV charging time series based on user activity patterns. Coupled with manufacturers’ data for representative EV models, the developed probabilistic model converts single user activity profiles into electrical demand, which can then be aggregated to simulate larger numbers at a neighbourhood, city or regional level. The EV charging demand model has been integrated into a domestic electrical demand model previously developed by researchers in our group at the University of Edinburgh. The integrated model is used to show how demand management can be used to assist voltage regulation in the distribution system. The node voltage sensitivity method is used to optimise the planning of EV charging based on the influence that every EV charger has on the network depending on their point of connection. The model and the charging strategy were tested on a realistic “highly urban” low voltage network and the results obtained show that voltage fluctuation due to the high percentage of EV ownership (and charging) can be significantly and maintained within the statutory range during a full 24-hour cycle of operation. The developed model is also used to assess the generation cost as well as the environmental impact, in terms of GHG emissions, as a result of EV charging, and an optimisation algorithm has been developed that in combination with domestic demand management, minimises the incurred costs and GHG emissions. The obtained results indicate that although the increased population of EVs in distribution networks will stress the system and have adverse economic and environmental effects, these may be minimised with careful off-line planning

A design scheme of energy management,control,optimisation system for hybrid solar-wind and battery energy storages system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Hybrid renewable energy system was introduced to improve the individual renewable energy power system’s productivity and operation-ability. This circumstance has led towards an extensive technological study and analysis on the hybrid renewable energy system. The extensive technological study is conducted using many different approaches, but in this research the linear programming, artificial intelligence and smart grid approaches are studied. This thesis proposed a complete hardware system development, implementation and construction of real-time DC Hybrid Renewable Energy System for solar-wind-battery energy source integrated with grid network support. The proposed real-time DC HRES hardware system adopts the hybrid renewable energy system concept which is composed of solar photovoltaic, wind energy system, battery energy storage system and grid network support. The real-time DC HRES hardware system research work is divided into three stages. Stage 1 involves modelling and simulation of the proposed system using MATLAB Simulink/Stateflow software. During this stage, system’s methodological design and development is emphasised. The obtained results are considered as fundamental finding to design, develop, integrate, implement and construct the real-time DC HRES hardware system. Stage II is designing and developing the electronic circuits for the real-time DC HRES hardware system using PROTEUS software. Real time simulation is performed on the electronic circuits to study and analyse the circuit’s behaviour. This stage also involves embedded software application development for the microcontroller PIC16F877A. Thus, continuous dynamic decision-making algorithm is developed and incorporated into microcontroller PIC16F877A. Next, electronic circuits and continuous dynamic decision-making algorithm are integrated with the microcontroller PIC16F877A as a real-time DC HRES hardware system to perform real time simulation. The real-time DC HRES hardware system simulation results are studied, analysed and compared with the results obtained in Stage 1. Any indifference between the obtained results in Stage 1 and Stage 2 are analysed and necessary changes are made. Stage 3 involves integrating, implementation and construction of real-time DC HRES. The continuous dynamic decision-making algorithm is also incorporated into the real microcontroller PCI16F877A development board. Real-time DC HRES’s experimental results have successfully demonstrated the system’s ability to perform supervision, coordination, management and control of all the available energy sources with lease dependency on the grid network. The obtained results demonstrated the energy management and optimisation of the available energy sources as primary power source deliver

Modification and experimental calibration of ADM1 for modelling the anaerobic digestion of solid wastes in demand driven applications

This thesis is an exploration into the modelling of anaerobic digestion (AD) with a focus on its integration into a microgrid for rural electrification. The work investigated the improvement of Anaerobic Digestion Model No 1 (ADM1) in order to better describe the kinetics of biogas production in an AD system with particular focus on substrate characterisation, codigestion and the mechanisms of inhibition. The resulting model was used to investigate the possible role of AD in microgrid systems. A novel biochemical and kinetic fractionation method was developed in order to fully characterise any substrate and produce the required input parameters into the a modified version of ADM1. The method uses a combination of analytical and digestion batch tests and was applied to food waste, green waste, pig manure and oat processing residues. The fractionation method was validated using measurements from semi-continuous laboratory scale digesters, operated with varying substrate combinations and loading rates. The model was able to suitably predict the methane production rate and the typical off-line measurements in AD systems, except during periods of high organic loading rate where biochemical inhibition became an important phenomenon. Possible inhibiting mechanisms were investigated by model based analysis of the experimental data characterised by inhibition, and a possible inhibition mechanism was proposed and integrated in the ADM1 model. Microgrid modelling software HOMER was used alongside the updated version of ADM1 in order to perform a benchmark of various operational and control strategies for the demand-driven operation of an AD system integrated in a microgrid. Different biogas demand profiles were considered. In the case of a biogas demand profile with low variability it was found that simple operational strategies could be used, with limited required biogas storage buffer and without causing process instabilities. With more variable demand profiles, an expert control system was needed in order to reduce the biogas storage requirements and guarantee process stability