A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction

Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models

The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants

Optimal Management of an Integrated Electric Vehicle Charging Station under Weather Impacts

The focus of this Dissertation is on developing an optimal management of what is called the “Integrated Electric Vehicle Charging Station” (IEVCS) comprising the charging stations for the Plug-in Electric Vehicles (PEVs), renewable (solar) power generation resources, and fixed battery energy storage in the buildings. The reliability and availability of the electricity supply caused by severe weather elements are affecting utility customers with such integrated facilities. The proposed management approach allows such a facility to be coordinated to mitigate the potential impact of weather condition on customers electricity supply, and to provide warnings for the customers and utilities to prepare for the potential electricity supply loss. The risk assessment framework can be used to estimate and mitigate such impacts. With proper control of photovoltaic (PV) generation, PEVs with mobile battery storage and fixed energy storage, customers’ electricity demand could be potentially more flexible, since they can choose to charge the vehicles when the grid load demand is light, and stop charging or even supply energy back to the grid or buildings when the grid load demand is high. The PV generation capacity can be used to charge the PEVs, fixed battery energy storage system (BESS) or supply power to the grid. Such increased demand flexibility can enable the demand response providers with more options to respond to electricity price changes. The charging stations integration and interfacing can be optimized to minimize the operational cost or support several utility applications

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Customer engagement strategies in retail electricity markets: a comprehensive and comparative review

Retail electricity markets require development to ensure efficient and equitable pass through of wholesale electricity costs to customers. Customer engagement has been heralded as a concept to improve the wholesale-to-retail link, better harness flexible demand loads and co-ordinate distributed renewable generation and storage. This study reviews the state-of-the-art customer engagement trends in retail electricity markets, and in doing so, it first establishes a definition of customer engagement in the context of retail electricity markets. Second, the paper identifies that literature on customer engagement revolves around three key strategic themes, namely ‘Customer Focus’, ‘Tariff Design’ and ‘Innovation’. Third, the paper systematically provides a comprehensive review of these customer engagement strategies in retail electricity markets. Finally, the study identifies the technical, market and social requirements to deliver an innovative retail electricity market structure to decarbonise society. This paper's crucial and novel policy recommendation is that integrating market mechanisms and technology (i.e. cross-linking across the three customer engagement strategy themes) is required to ensure robust and efficient retail electricity market operation as society advances to a net zero economy. The study concludes with the establishment of eight future research directions of customer engagement for retail electricity market design

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

MODELING AND ASSESSING THE SUSTAINABILITY OF DISTRIBUTED SOLAR PHOTOVOLTAICS ADOPTION

Participation of distributed solar photovoltaic (PV) generation in the organized electricity wholesale market is expected to increase under the Federal Energy Regulatory Commission Order 2222 announced in 2020. Our understanding about the technical, economic, and environmental tradeoffs and co-benefits of solar PV adoption on both building and regional scales remains limited, especially considering the complexity of varied distributed solar PV-battery system designs and operation strategies as well as the dynamic interactions of these distributed generations with the centralized grid. This dissertation therefore aims to investigate the grid load reduction, life cycle cost, and life cycle environmental (e.g., carbon, water, and energy footprints) performances of typical distributed PV systems considering their dynamic interactions with the centralized grid. This dissertation intends to examine the possible scenarios in which future adoption of PV systems can facilitate economic saving, reduce environmental footprints, relieve centralized grid stress, and supplement differential electricity demands of residential energy users on both building and city scales. To this end, a modeling framework was developed consisting of a stochastic residential electricity demand model, a system dynamics model of solar energy generation, energy balance, storage, and selling, and life cycle economic and environmental assessment model. The stochastic residential electricity demand simulation considered five typical types of household occupants and eight types of households. The generated solar energy, grid supply, and residential demand were balanced for each residential building using energy balance model. This model was further scaled up to a city level using Boston, MA as a testbed. On the building level, we found a clear tradeoff between the life cycle cost and environmental savings when sizing the PV systems differently. Moreover, installing a solar PV-battery system but without an effective control strategy can result in sub-optimized peak-load reduction, economic, and environmental outcomes. Installing solar PV-battery systems with proper controls can achieve the highest on-peak load reductions and economic benefits under the time-of-use utility rate design. However, they do not necessarily provide the highest environmental benefits, indicating a potential technical, environmental, and economic tradeoff. Our regional analysis found a large penetration of solar PV systems may result in a steeper ramp-up of the grid load during winter days, but it may provide load-shedding benefits during summer days. Large buildings may perform the best technically and environmentally when adopting solar PV systems, but they may have higher life cycle costs