Reallocating charging loads of electric vehicles in distribution networks

In this paper, the charging loads of electric vehicles were controlled to avoid their impact on distribution networks. A centralized control algorithm was developed using unbalanced optimal power flow calculations with a time resolution of one minute. The charging loads were optimally reallocated using a central controller based on non-linear programming. Electric vehicles were recharged using the proposed control algorithm considering the network constraints of voltage magnitudes, voltage unbalances, and limitations of the network components (transformers and cables). Simulation results showed that network components at the medium voltage level can tolerate high uptakes of uncontrolled recharged electric vehicles. However, at the low voltage level, network components exceeded their limits with these high uptakes of uncontrolled charging loads. Using the proposed centralized control algorithm, these high uptakes of electric vehicles were accommodated in the network under study without the need of upgrading the network components

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources

Optimal battery storage operation for PV systems with tariff incentives

Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery storage unit cost. An electricity customer whose electricity demand is supplied by a grid connected PV generation system benefiting from a FiT incentive is simulated in this paper. The system is simulated with the PV modelled as an existing system and the PV modelled as a new system. For a better understanding of the existing PV system with battery storage operation, an optimisation problem was formulated which resulted in a mixed integer linear programming (MILP) problem. The optimisation model was developed to solve the MILP problem and to analyse the benefits considering different electricity tariffs and battery storage in maximising FiT revenue streams for the existing PV generating system. Real data from a typical residential solar PV owner is used to study the benefit of the battery storage system using half-hourly dataset for a complete year. A sensitivity analysis of the MILP optimisation model was simulated to evaluate the impact of battery storage capacity (kWh) on the objective function. In the second case study, the electricity demand data, solar irradiance, tariff and battery unit cost were used to analyse the effect of battery storage unit cost on the adoption of electricity storage in maximising FiT revenue. In this case, the PV is simulated as a new system using Distributed Energy Resources Customer Adoption Model (DER-CAM) software tool while modifying the optimisation formulation to include the PV onsite generation and export tariff incentive. The results provide insights on the benefit of battery storage for existing and new PV system benefiting from FiT incentives and under time-varying electricity tariffs

A cloud-based energy management system for building managers

A Local Energy Management System (LEMS) is described to control Electric Vehicle charging and Energy Storage Units within built environments. To this end, the LEMS predicts the most probable half hours for a triad peak, and forecasts the electricity demand of a building facility at those times. Three operational algorithms were designed, enabling the LEMS to (i) flatten the demand profile of the building facility and reduce its peak, (ii) reduce the demand of the building facility during triad peaks in order to reduce the Transmission Network Use of System (TNUoS) charges, and (iii) enable the participation of the building manager in the grid balancing services market through demand side response. The LEMS was deployed on over a cloud-based system and demonstrated on a real building facility in Manchester, UK

Scenarios for the development of smart grids in the UK: synthesis report

‘Smart grid’ is a catch-all term for the smart options that could transform the ways society produces, delivers and consumes energy, and potentially the way we conceive of these services. Delivering energy more intelligently will be fundamental to decarbonising the UK electricity system at least possible cost, while maintaining security and reliability of supply. Smarter energy delivery is expected to allow the integration of more low carbon technologies and to be much more cost effective than traditional methods, as well as contributing to economic growth by opening up new business and innovation opportunities. Innovating new options for energy system management could lead to cost savings of up to £10bn, even if low carbon technologies do not emerge. This saving will be much higher if UK renewable energy targets are achieved. Building on extensive expert feedback and input, this report describes four smart grid scenarios which consider how the UK’s electricity system might develop to 2050. The scenarios outline how political decisions, as well as those made in regulation, finance, technology, consumer and social behaviour, market design or response, might affect the decisions of other actors and limit or allow the availability of future options. The project aims to explore the degree of uncertainty around the current direction of the electricity system and the complex interactions of a whole host of factors that may lead to any one of a wide range of outcomes. Our addition to this discussion will help decision makers to understand the implications of possible actions and better plan for the future, whilst recognising that it may take any one of a number of forms

Computational resource management for data-driven applications with deadline constraints

Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user-defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints

Scenarios for the Development of Smart Grids in the UK: synthesis report

‘Smart grid’ is a catch-all term for the smart options that could transform the ways society produces, delivers and consumes energy, and potentially the way we conceive of these services. Delivering energy more intelligently will be fundamental to decarbonising the UK electricity system at least possible cost, while maintaining security and reliability of supply. Smarter energy delivery is expected to allow the integration of more low carbon technologies and to be much more cost effective than traditional methods, as well as contributing to economic growth by opening up new business and innovation opportunities. Innovating new options for energy system management could lead to cost savings of up to £10bn, even if low carbon technologies do not emerge1. This saving will be much higher if UK renewable energy targets are achieved. Building on extensive expert feedback and input, this report describes four smart grid scenarios which consider how the UK’s electricity system might develop to 2050. The scenarios outline how political decisions, as well as those made in regulation, finance, technology, consumer and social behaviour, market design or response, might affect the decisions of other actors and limit or allow the availability of future options. The project aims to explore the degree of uncertainty around the current direction of the electricity system and the complex interactions of a whole host of factors that may lead to any one of a wide range of outcomes. Our addition to this discussion will help decision makers to understand the implications of possible actions and better plan for the future, whilst recognising that it may take any one of a number of forms