1,720,973 research outputs found
Game Theoretical Control Frameworks for Multiple Energy Storage Services in Energy Communities
No full textIn the last decade, distributed energy generation and storage have significantly contributed to the widespread of energy communities. In this context, we propose an energy community model constituted by prosumers, characterized by their own demand and renewable generation, and service-oriented energy storage providers, able to store energy surplus and release it upon a fee payment. We address the problem of optimally schedule the energy flows in the community, with the final goal of making the prosumers' energy supply more efficient, while creating a sustainable and profitable business model for storage providers. The proposed resolution algorithms are based on decentralized and distributed game theoretical control schemes. These approaches are mathematically formulated and then effectively validated and compared with a centralized method through numerical simulations on realistic scenarios
Nonpharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread
No full textThis article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale
On Controlling Battery Degradation in Vehicle-to-Grid Energy Markets
No full textNowadays, power grids are facing reduced total system inertia as traditional generators are phased out in favor of renewable energy sources. This issue is expected to deepen with the increasing penetration of electric vehicles (EVs). The influence of a single EV on power networks is low; nevertheless, the aggregate impact becomes relevant when they are properly coordinated. In this context, we consider the frequent case of a group of EVs connected to a parking lot with a photovoltaic facility. We propose a novel strategy to optimally control their batteries during the parking session, which is able to satisfy their requirements and energy constraints. EVs participate in a noncooperative energy market based on a smart pricing mechanism that is designed in order to increase the predictability and flexibility of the aggregate parking load. Differently from the existing contributions, we employ a novel approach to minimize the degradation of batteries. The effectiveness of the proposed method is validated through numerical experiments based on a real scenario
Predictive energy scheduling of smart parking infrastructure with solar-powered electric vehicles
No full textThis paper presents a novel model predictive control framework for managing energy flow in smart parking infrastructures with renewable energy facilities, electric vehicles, and solar-powered electric vehicles. The proposed control framework minimizes the energy costs for the parking lot operators, ensuring the user-defined charge levels for vehicles at departure, and protecting the charging infrastructure during operation. Field validation on Lonsdale Street, Melbourne (Australia)—using real data on vehicle behavior, solar irradiance, and energy prices—shows significant grid load reduction even with partial solar production. Compared to a rule-based strategy, the MPC approach reduces operational costs by 15.32% and energy demand by 6.12%. Lastly, we show that the proposed framework is robust under forecast uncertainty, supporting its practical deployment in dynamic real-world environments
A Markowitz Optimization Approach for Automating the Italian Research Quality Monitoring and Evaluation
No full textThis paper presents a selection-supporting framework which can help research institutions to optimally and automatically pool the research products for research quality assessment programs, with a specific focus on the Italian evaluation process (VQR). After providing a mathematical description of the VQR exercise at the institutional level, we formulate the robust optimization problem which yields the optimal pair of research products and associated authors. We show how such a formulation quickly becomes unpractical, due to combinatorial issues, and propose a novel Markowitz-based alternative approach, which preserves computational feasibility and effectiveness. We strive to propose a reusable framework, not too tightly connected to the ruleset of the current VQR session (2020-2024). Finally, we validate the proposed framework on a synthetic set of parameters, which mimics a medium-sized research institution, with the aim of checking the computational feasibility of the proposed Markowitz-based variants
Noncooperative Control of Energy Communities through Learning-based Response Dynamics
No full textWith the growing availability of data, learning-based distributed energy management is emerging as a viable and efficient alternative to traditional model-based schemes. In this context, we propose a novel game-theoretic learning-based method for the distributed control of energy communities. In particular, we consider a community that includes several prosumers equipped with a renewable energy source and an energy storage system. The scheduling of energy activities of all prosumers is formulated as a noncooperative game. Nevertheless, unlike the state-of-the-art, where an optimization problem is typically defined to model the behavior of each prosumer, we approximate each prosumer response strategy using a neural network. We propose a distributed algorithm based on the well-known Banach-Picard iteration to efficiently seek for an equilibrium of the game. Lastly, the convergence and effectiveness of the proposed approach are validated through numerical simulations under different realistic scenarios
A Decentralized Noncooperative Control Approach for Sharing Energy Storage Systems in Energy Communities
No full textThis paper focuses on the optimal scheduling of the charging and discharging strategies of a community energy storage (CES) system, which is shared by the prosumers belonging to a grid-connected energy community. The prosumers own renewable energy sources (RESs), while they can buy/sell their energy imbalance directly from/to the power grid. For the sake of increasing the penetration of RESs and reducing the operating cost, prosumers leverage on the shared CES: in particular, each user can only employ a portion of the overall CES charge/discharge profile. Differently from the related literature, where storage devices are individually owned and the battery degradation aspects are typically disregarded, we propose a novel control mechanism based on noncooperative game theory, which allows users to minimize their energy cost as well as concur on the CES resources allocation with minimal-degradation. The effectiveness of the method is validated through numerical experiments on a realistic case study, where a shared CES supplies energy to the local community of residential prosumers. Finally, the comparison with a centralized control approach shows that the proposed framework allows all prosumers to achieve a fair cost-optimal utilization of the shared CES
Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration
No full textModel predictive control for thermal comfort optimization in building energy management systems
No full textModel Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger's Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats
Energy Consumption Optimisation for Horticultural Facilities
No full textThis paper proposes a framework designed to optimise energy consumption in vertical farming. It aims to maximise cost efficiency by balancing between minimising system operations during the electricity price peaks and the ability to trade capacity on the FCR market while also fulfilling constraints on the internal growing process. We consider that the vertical farming system has distributed control with a series of actuators controlled by various spatially distributed PLCs that we refer to as agents, to underline their independence. The paper conducts two experiments for a lO-agent system with a Pareto controller and a lOO-agent system with a Lagrangian approach and shows the balance between more cost-efficient momentary energy consumption control
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