Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

The need for new business models to replace existing ones, soon obsolete, is a subject often discussed among researchers in the area. It is essential to find a practical solution that includes the concepts of demand response and distributed generation in the energy markets, these being the future of the electricity grid. It is believed that these resources can bring advantages to the operation of the system, namely increasing technical efficiency. However, one of the problems is the aggregation of small resources as a result of the associated uncertainties. The authors propose a business model with three main phases used in planning: optimal scheduling, aggregation, and remuneration. In this paper, a new phase was added, the classification, with the main purpose of assisting the aggregator of these small resources in operating situations. The focus is on the fair remuneration of participants in the management of the market, in addition to minimizing operating costs. After testing four different remuneration methods, it was proved that the method proposed by the authors obtained better results, proving the viability of the proposed model.The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Platform-Based Business Models: Insights from an Emerging Ai-Enabled Smart Building Ecosystem

Artificial intelligence (AI) is emerging to become a highly potential enabling technology for smart buildings. However, the development of AI applications quite often follows a traditional, closed, and product-oriented approach. This study aims to introduce the platform model and ecosystem thinking to the development of AI-enabled smart buildings. The study identifies the needs for a user-oriented digital service ecosystem and business model in the smart building sector in Finland, which aimed to facilitate the launch of scalable businesses and an experiential and dynamic business ecosystem. A multi-method, interpretive case study was applied in the focal ecosystem, with the leading real estate and facility management operators in Northern Europe as part of a Finnish national innovation project. Our results propose an extended comprehensive framework of the 5C ecosystemic model (Connection, Content, Computation, Context, and Commerce) and the possible paths of ecosystem players in the domain of smart building and smart built environment, both theoretically and empirically. The platform-oriented business models are missing, yet desired, by the ecosystem actors. The value chain and ecosystem platforms imply the quest for new (platform) models. Finally, our research discusses the need for new value-chain- and ecosystem-oriented AI development and big data platforms in the future.Keywords: smart building; artificial intelligence; platform; ecosystem; business model</div

An Embedded Platform for Testbed Implementation of Multi-Agent System in Building Energy Management System

This paper presents a hardware testbed for testing the building energy management system (BEMS) based-on the multi agent system (MAS). The objective of BEMS is to maximize user comfort while minimizing the energy extracted from the grid. The proposed system implements a multi-objective optimization technique using a genetic algorithm (GA) and the fuzzy logic controller (FLC) to control the room temperature and illumination setpoints. The agents are implemented on the low cost embedded systems equipped with the WiFi communication for communicating between the agents. The photovoltaic (PV)-battery system, the air conditioning system, the lighting system, and the electrical loads are modeled and simulated on the embedded hardware. The popular communication protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus TCP/IP are adopted for integrating the proposed MAS with the existing infrastructures and devices. The experimental results show that the sampling time of the proposed system is 16.50 s. Therefore it is suitable for implementing the BEMS in a real-time where the data are updated in an hourly or minutely basis. Further, the proposed optimization technique shows better results in optimizing the comfort index and the energy extracted from the grid compared to the existing methods. Keywords: BEMS; MAS; embedded system; multi-objective optimization; genetic algorith

Sistem Smart Grid Untuk Optimalisasi Pemakaian Daya Listrik Pada Perumahan Dan Gedung Dengan Pemanfaatan Energi Surya

Sistem Smart Grid merupakan teknologi kelistrikan terkini yang mampu mengalirkan arus listrik dan informasi secara dua arah, dari pembangkit ke konsumen dan sebaliknya. Kemajuan teknologi ini mulai banyak diimplementasikan dalam pengelolaan energi listrik, salah satunya integrasi dengan sumber energi terbarukan. Salah satu permasalahan yang banyak ditemui dalam bidang kelistrikan adalah manajemen energi listrik. Pada penelitian ini, peneliti merancang model kelistrikan modern (Smart Grid) untuk manajemen energi di perumahan dan gedung-gedung dalam rangka pengembangan sistem Smart Home dan Smart Building. Penelitian yang dikembangkan akan mengoptimalkan pemakaian energi listrik secara real-time tergantung kondisi beban dan pembangkit energi yang ada saat itu. Pada tahun pertama dirancang model sistem Smart Grid untuk optimalisasi pemakaian daya listrik rumah (TKT-3). Sedangkan pada tahun kedua dirancang model sistem Smart Grid untuk optimalisasi pemakaian daya listrik gedung (TKT-3). Dengan sistem yang dikembangkan ini, diharapkan pemanfaatan, pengelolaan energi listrik utamanya yang bersumber dari energi surya dapat dimaksimalkan, dan sekaligus merupakan upaya pencapaian sasaran Renstra penelitian perguruan tinggi terutama pada bidang unggulan energi baru dan terbarukan

Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage

The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.Ph.D