Incentive Design for Direct Load Control Programs

We study the problem of optimal incentive design for voluntary participation of electricity customers in a Direct Load Scheduling (DLS) program, a new form of Direct Load Control (DLC) based on a three way communication protocol between customers, embedded controls in flexible appliances, and the central entity in charge of the program. Participation decisions are made in real-time on an event-based basis, with every customer that needs to use a flexible appliance considering whether to join the program given current incentives. Customers have different interpretations of the level of risk associated with committing to pass over the control over the consumption schedule of their devices to an operator, and these risk levels are only privately known. The operator maximizes his expected profit of operating the DLS program by posting the right participation incentives for different appliance types, in a publicly available and dynamically updated table. Customers are then faced with the dynamic decision making problem of whether to take the incentives and participate or not. We define an optimization framework to determine the profit-maximizing incentives for the operator. In doing so, we also investigate the utility that the operator expects to gain from recruiting different types of devices. These utilities also provide an upper-bound on the benefits that can be attained from any type of demand response program.Comment: 51st Annual Allerton Conference on Communication, Control, and Computing, 201

Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation

Improved quality of online education using prioritized multi-agent reinforcement learning for video traffic scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of videobased online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi- Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-theart solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results

Energy Management of Distributed Generation Systems

The book contains 10 chapters, and it is divided into four sections. The first section includes three chapters, providing an overview of Energy Management of Distributed Systems. It outlines typical concepts, such as Demand-Side Management, Demand Response, Distributed, and Hierarchical Control for Smart Micro-Grids. The second section contains three chapters and presents different control algorithms, software architectures, and simulation tools dedicated to Energy Management Systems. In the third section, the importance and the role of energy storage technology in a Distribution System, describing and comparing different types of energy storage systems, is shown. The fourth section shows how to identify and address potential threats for a Home Energy Management System. Finally, the fifth section discusses about Economical Optimization of Operational Cost for Micro-Grids, pointing out the effect of renewable energy sources, active loads, and energy storage systems on economic operation

EV charging stations and RES-based DG: A centralized approach for smart integration in active distribution grids

Renewable Energy Sources based (RES-based) Dispersed Generation (DG) and Electrical Vehicles (EVs) charging systems diffusion is in progress in many Countries around the word. They have huge effects on the distribution grids planning and operation, particularly on MV and LV distribution grids. Many studies on their impact on the power systems are ongoing, proposing different approaches of managing. The present work deals with a real application case of integration of EVs charging stations with ES-based DG. The final task of the integration is to be able to assure the maximum utilization of the distribution grid to which both are connected, without any upgrading action, and in accordance with Distribution System Operators (DSOs) needs. The application of the proposed approach is related to an existent distribution system, owned by edistribuzione, the leading DSO in Italy. Diverse types of EVs supplying stations, with diverse diffusion scenarios, have been assumed for the case study; various Optimal Power Flow (OPF) models, based on diverse objective functions, reflecting DSO necessities, have been applied and tried. The obtained results demonstrate that a centralized management approach by the DSO, could assure the respect of operation limits of the system in the actual asset, delaying or avoiding upgrading engagements and charges

Efficient Energy Optimization for Smart Grid and Smart Community

The electric power industry has undergone significant changes in response to the environmental concerns during the past decades. Nowadays, due to the integration of different distributed energy systems in the smart grid, the balancing between power generation and load demand becomes a critical problem. Specifically, due to the intermittent nature of renewable energy sources (RESs) , power system optimization becomes significantly complicated. Due to the uncertain nature of RESs, the system may fail to ensure the power quality which may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. This dissertation presents three projects to study the optimization and control for smart grid and smart community. First, optimal operation of battery energy storage system (BESS) in grid-connected microgrid is studied. Near optimal operation/allocation of the BESS is investigated with the consideration of battery lifetime characteristics. Approximate dynamic programming (ADP) is proposed to solve optimal control policy for time-dependent and finite-horizon BESS problems and performance comparison is done with classical dynamic programming approach. The results show that the ADP can optimize the system operation under different scenarios to maximize the total system revenue. Second, optimal operation of the BESS in islanded microgrid is also studied. Specifically, a new islanded microgrid model is formulated based on Markov decision process. A computationally efficient ADP approach is proposed to solve this energy optimization problem, and achieve near minimum operational cost efficiently. Simulation results show that the proposed ADP can achieve 100% and at least 98% of optimality for deterministic and stochastic case studies, respectively. The performance of the proposed ADP approach also achieved 18:69 times faster response than that of the traditional DP approach for 0:5 million of data samples. Third, a demand side management technique is proposed for the optimization of residential demands with financial incentives. A new design of comfort indicator is proposed considering both thermal and other electric appliances based on consumers’ comfort level. The proposed approach is compared with two existing demand response approaches for both 10-houses and 100-houses simulation studies. For both cases, the proposed approach outperformed the existing approaches in terms of reward incentives and comfort levels

Plug-and-Play Distributed Algorithms for Optimized Power Generation in a Microgrid

This paper introduces distributed algorithms that share the power generation task in an optimized fashion among the several Distributed Energy Resources (DERs) within a microgrid. We borrow certain concepts from communication network theory, namely Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, which are known to be convenient in terms of communication requirements and network efficiency.We adapt the synchronized version of AIMD to minimize a cost utility function of interest in the framework of smart grids. We then implement the AIMD utility optimisation strategies in a realistic power network simulation in Matlab-OpenDSS environment, and we show that the performance is very close to the full-communication centralized case