Development of Design Optimization for Smart Grid (DOfSG) Framework for Residential Energy Efficiency via Fuzzy Delphi Method (FDM) Approach

The smart grid revolution has benefited many sectors but the potential for design optimization among residential units has yet to be explored. Despite some researchers having negative perception of house design's association with the smart grid system, there is in fact potential for investigating design attribute optimisation aligned with the smart grid system. As electricity becomes a necessity of the 21st century society, residential dwellers are becoming more dependent on this indispensable source of energy. As such, this paper explains the development of a framework focusing on design optimization for residential units aligned to the smart grid system using the Fuzzy Delphi Method approach. It focuses on the significant smart grid components linked to the residential sector incorporating key design attributes for energy optimization purposes. The proposed framework denoted two main components of residential design optimization, depicted as indoor and outdoor parameters with its subsequent attributes further categorised into main and detailed components. Twelve design parameters were found to be substantial for the DOfSG development, intended to provide useful guide for aligning residential design towards the smart grid system in Malaysia

Operation and Planning of Energy Hubs Under Uncertainty - a Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs

Microgrid design, control, and performance evaluation for sustainable energy management in manufacturing

This research studies the capacity sizing, control strategies, and performance evaluation of the microgrids with hybrid renewable sources for manufacturing end use customers towards a distributed sustainable energy system paradigm. Microgrid technology has been widely investigated and applied in commercial and residential sector, while for manufacturers, it has been less explored and utilized. To fill the gap, the dissertation first proposes a cost-effective sizing model to identify the capacities as well as control strategies of the components in microgrids considering a commonly used energy tariff, i.e., Time of Use (TOU). Then, the sizing model is extended by integrating control strategies for both microgrid components and manufacturing systems considering a typical demand response program, i.e., Critical Peak Pricing (CPP), where customer side load adjustment is highly encouraged. After that, the control strategy of the manufacturers in an overgeneration mitigation-oriented demand response program is further investigated based on the identified optimal size of onsite microgrid to minimize the energy cost. Later, the system is analyzed from its higher level of abstraction where a prosumer community is developed by aggregating such manufacturers with onsite microgrid system. To enhance the reliable energy operation in the community, the performance of the microgrid is investigated through the estimation of the lifetime of Battery Energy Storage System (BESS), a critical design parameter the architecture. Finally, conclusions are presented and future research on real-time joint control strategy for both microgrids and manufacturing systems and identification as well as optimal energy management of the controllable loads in manufacturing system are discussed --Abstract, page iii

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally

Modeling and sensitivity analysis of grid-connected hybrid green microgrid system

The demonstrated research work analyses the technoeconomic modelling and sensitivity analysis of the available resources for the rural community in India. The various resources used in this study are solar, wind, hydro, battery and utility grid-connected system. The usefulness of the on-grid system in the rural sector is that excess amount of electricity produced through renewable energy sources (RES) could be sold back to the utility grid. A total of 12 possible configurations of various resources with and without a grid-connected system was analyzed for minimum Levelized Cost of Energy (LCOE) and Total Net Present Cost (TNPC). Further, sensitivity analysis is accomplished for different sensitive variables to understand the nature of the system for wider application in rural communities. The solar-wind-hydro-based utility grid-connected network is observed to be the best optimal configuration with a minimum value of LCOE of 0.056 $/kWh. The simulation results reveal that the effective utilization of RES has been a cost-efficient and reliable system to the power supply in remote communities

Using genetic algorithm for optimal sizing of stand-alone hybrid energy system

When planning a hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources—those that rely on fossil fuels—the primary considerations are the total cost of the system and the CO? emissions. In this paper, we will investigate the typical hybrid energy system (HES) that incorporates both renewable and non-renewable energy sources involving a detailed simulation process that may require specific inputs, models, and data. Then, we employed dual optimization methods: genetic algorithm (GA) and particle swarm optimization (PSO). The consequences of GA and PSO execution in the bus timetabling problem depict that the GA algorithm is better at finding the optimal solution in terms of accuracy and iteration. Additionally, the GA algorithm is also superior to the straightforwardness of the techniques used. So, in this work, we employed a Genetic Algorithm Optimization (GA)–-based optimal sizing technique for HES configurations that include sustainability wind turbines (WTs), battery storage (BS), and diesel generators (DGs). HES improved power delivery to a rural community in the Wasit Province, Iraq, situated at 46° - 36° and 32° - 31° in the country's southeastern central region. Throughout the project's 25-year lifespan, the optimization primarily aims to minimize the total cost (CT) and total CO? emissions (ECO2T). The outcomes demonstrate that the GA algorithm may, with continuous electricity supply, minimize the objectives while meeting the load demand

Techno-Economic modelling of hybrid renewable mini-grids for rural electrification planning in Sub-Saharan Africa

Access to clean, modern energy services is a necessity for sustainable development. The UN Sustainable Development Goals and SE4ALL program commit to the provision of universal access to modern energy services by 2030. However, the latest available figures estimate that 1.1 billion people are living without access to electricity, with over 55% living in Sub-Saharan Africa. Furthermore, 85% live in rural areas, often with challenging terrain, low income and population density; or in countries with severe underinvestment in electricity infrastructure making grid extension unrealistic. Recently, improvements in technology, cost efficiency and new business models have made mini-grids which combine multiple energy technologies in hybrid systems one of the most promising alternatives for electrification off the grid. The International Energy Agency has estimated that up to 350,000 new mini-grids will be required to reach universal access goals by 2030. Given the intermittent and location-dependent nature of renewable energy sources, the evolving costs and performance characteristics of individual technologies, and the characteristics of interacting technologies, detailed system simulation and demand modelling is required to determine the cost optimal combinations of technologies for each-and-every potential mini-grid site. Adding to this are the practical details on the ground such as community electricity demand profiles and distances to the grid or fuel sources, as well asthe social and political contexts,such as unknown energy demand uptake or technology acceptance, national electricity system expansion plans and subsidies or taxes, among others. These can all have significant impacts in deciding the applicability of a mini-grid within that context. The scope of the research and modelling framework presented focuses primarily on meeting the specific energy needs in the sub-Saharan African context. Thus, in being transparent, utilizing freely available software and data as well as aiming to be reproducible, scalable and customizable; the model aims to be fully flexible, staying relevant to other unique contexts and useful in answering unknown future research questions. The techno-economic model implementation presented in this paper simulates hourly mini-grid operation using meteorological data, demand profiles, technology capabilities, and costing data to determine the optimal component sizing of hybrid mini-grids appropriate for rural electrification. The results demonstrate the location, renewable resource, technology cost and performance dependencies on system sizing. The model is applied for the investigation of 15 hypothetical mini-grids sites in different regions of South Africa to validate and demonstrate the model’s capabilities. The effect of technology hybridization and future technology cost reductions on the expected cost of energy and the optimal technology configurations are demonstrated. The modelling results also showed that the combination of hydrogen fuel cell and electrolysers was not an economical energy storage with present day technology costs and performance. Thereafter, the model was used to determine an approximate fuel cell and electrolyser cost target curve up to the year 2030. Ultimately, any research efforts through the application of the model, building on the presented framework, are intended to bridge the science-policy boundary and give credible insight for energy and electrification policies, as well as identifying high impact focus areas for ongoing further research