Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Capturing Aggregate Flexibility in Demand Response

Flexibility in electric power consumption can be leveraged by Demand Response (DR) programs. The goal of this paper is to systematically capture the inherent aggregate flexibility of a population of appliances. We do so by clustering individual loads based on their characteristics and service constraints. We highlight the challenges associated with learning the customer response to economic incentives while applying demand side management to heterogeneous appliances. We also develop a framework to quantify customer privacy in direct load scheduling programs.Comment: Submitted to IEEE CDC 201

A novel energy management system for optimal energy and flexibility scheduling of residential buildings: a case study in HSB Living Lab

The future distribution system needs more flexibility to handle the peak power demand arising from the electrification of heating and transportation. This paper proposes a novel energy management system (EMS) for residential buildings to optimize their electric and heat consumptions while offering flexibility in response to the requirements of the Distribution System Operator (DSO). The aim of the proposed EMS is to minimize the energy and peak power costs while simultaneously maximizing the revenue from offering flexibility. This is achieved through the optimal scheduling of battery energy storage charging and discharging as well as the efficient utilization of the heat pump. To cope with forecasting uncertainties, a rolling horizon-based algorithm with uncertainty modelling based on the chance constraint method is incorporated. The performance of the proposed EMS is investigated by simulating the daily operation of a real residential building. The case studies indicate that the scheduled flexibility can be successfully dispatched even in the presence of forecasting uncertainties, causing 6% reduction in the payment cost of the building

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Flexibility Potential of a Smart Home to Provide TSO-DSO-level Services

The high penetration of intermittent renewable-based power into modern power systems increases the need for more technical ancillary services from flexible energy resources. Smart homes could provide different flexibility services related to active power control services and therefore fulfill a part of the flexibility needs of system operators. In this regard, the estimation of the flexible capacities of each smart home's flexible device is of key importance. Correspondingly, this paper first estimates the flexible capacities of a smart home with controllable devices as flexible resources. The flexible capacity of each appliance is estimated considering its flexible and non-flexible operations. Besides, the local and system-wide flexibility services are introduced and the paper discusses whether a smart home can provide these types of services. In the simulations of this paper, the flexible capacity of each household appliance is estimated and compared to each other. Finally, the profitability of the smart home's battery energy storage multi-use is analyzed when it is providing three different types of flexibility services for the transmission system operator's needs. The results demonstrate that in some scenarios, the smart home's battery energy storage can increase its profits by providing transmission-system-level flexibility.© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)This work was supported in part by the FLEXIMAR Project (novel marketplace for energy flexibility) through Business Finland under Grant 6988/31/2018, and in part by the Finnish companies.fi=vertaisarvioitu|en=peerReviewed

A fuzzy logic control of a smart home with energy storage providing active and reactive power flexibility services

There is a need for enhanced flexibility to allow the high penetration of intermittent renewable power into the power system. In this way, transmission system operators (TSO) need more flexible energy resources that help to control the power system frequency by using balancing services. Distribution system operators (DSO) also seek new flexible energy resources that can counteract stochasticity, control voltage level, and manage congestions in distribution networks. Smart homes located in distribution networks are potential resources. Hence, this paper considers a smart home with flexible appliances and devices, including a battery energy storage system (BESS) interfaced with an inverter, an air conditioner (AC), and an electric vehicle (EV). The smart home aims to provide the system operators with coordinated frequency and DSO-level services while respecting the thermal comfort and schedules of the household residence. The inverter-interfaced BESS not only provides active power support for TSO and DSO, but it also injects and consumes reactive power if the DSO needs local flexibility. Fuzzy logic control system is deployed to obtain this goal. In the simulation section, a smart home with flexible appliances is scheduled. Different operations and the economic outcomes are discussed for the smart home considering real-world data.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed