Towards Smarter Electric Vehicle Charging with Low Carbon Smart Grids: Pricing and Control.

Environmental and political directions indicate transition to a decarbonized transportation system is necessary as it is one of the most pollutant sectors regarding greenhouse gas emissions. Research in Demand Side Management suggests that its tools are the most cost-effective option for improving the performance of the grid without incurring into high infrastructure investments, hence reducing the payback for start-ups in the sector. This Thesis proposes solutions to tackle 5 objectives around this area of research: 1-2 are related to developing a demand response pricing and EV smart charging strategies, 3-4 are related to developing a multi-objective charging scheme in order to ensure fairness and reduction of CO2eq emissions, and 5 is related to testing parameters of EV charging to understand future improvements and limitations in the proposed models. Chapter 3, that tackles objectives 1-2, proposes a data-driven optimisation algorithm with pricing and control modules that communicate with each other to achieve a successful integration with the grid by charging at the right price and expected time. The results show customers can be positively engaged with pricing signals while providing support to the grid. Chapter 4, which tackles objectives 3-4, proposes a multi-objective EV charging formulation that include perspectives of EV users, a carbon regulator and a charging station operator. The multi-objective formulation is solved with a genetic algorithm in order to find the fairest and the greenest solution. Results which are evaluated using different scenarios show different weights to each objective function can differ based on the charging location and EV charging availability. Finally, Chapter 5 which tackles objective 5, shows a sensitivity analysis where improvements in revenues, reduction of carbon emissions and bidding capacity depend on the evaluation of EV users’ parameters, and the charging station control and sizing

Competitive Online Peak-Demand Minimization Using Energy Storage

We study the problem of online peak-demand minimization under energy storage constraints. It is motivated by an increasingly popular scenario where large-load customers utilize energy storage to reduce the peak procurement from the grid, which accounts for up to $90\%$ of their electric bills. The problem is uniquely challenging due to (i) the coupling of online decisions across time imposed by the inventory constraints and (ii) the noncumulative nature of the peak procurement. In this paper, we develop an optimal online algorithm for the problem, attaining the best possible competitive ratio (CR) among all deterministic and randomized algorithms. We show that the optimal CR can be computed in polynomial time, by solving a linear number of linear-fractional problems. More importantly, we generalize our approach to develop an \emph{anytime-optimal} online algorithm that achieves the best possible CR at any epoch, given the inputs and online decisions so far. The algorithm retains the optimal worst-case performance and achieves adaptive average-case performance. Simulation results based on real-world traces show that, under typical settings, our algorithms improve peak reduction by over $19\%$ as compared to baseline alternatives

Review of Congestion Management Methods for Distribution Networks with High Penetration of Distributed Energy Resources

This paper reviews the existing congestion management methods for distribution networks with high penetration of DERs documented in the recent research literatures. The congestion management methods for distribution networks reviewed can be grouped into two categories – market methods and direct control methods. The market methods consist of dynamic tariff, distribution capacity market, shadow price and flexible service market. The direct control methods are comprised of network reconfiguration, reactive power control and active power control. Based on the review of the existing methods, the authors suggest a priority list of the existing methods

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Maximizing demand response aggregator compensation through optimal RES utilization : aggregation in Johannesburg, South Africa

Abstract:This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain

Research on economic planning and operation of electric vehicle charging stations

Appropriately planning and scheduling strategies can improve the enthusiasm of Electric vehicles (EVs), reduce charging losses, and support the power grid system. Thus, this dissertation studies the planning and operating of the EV charging station. First, an EV charging station planning strategy considering the overall social cost is proposed. Then, to reduce the charging cost and guarantee the charging demand, an optimal charging scheduling method is proposed. Additionally, by considering the uncertainty of charging demand, a data-driven intelligent EV charging scheduling algorithm is proposed. Finally, a collaborative optimal routing and scheduling method is proposed

Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

Most of the prosumers nowadays are constrained to trade only with the supplier under a flat tariff or dynamic time-of-use price signals. This paper models and discusses the cost-saving benefits of flexible prosumers as members of energy communities who can exchange electricity among peers and on the wholesale markets through a community manager. Authors propose a novel centralized post-process sharing method by introducing a two-stage mechanism which, unlike the existing methods, guarantees benefits for prosumers joining the energy community. The first stage assesses internal price calculation in three different methods: Bill Sharing Method Net (BSMN), Mid-Market Rate Net (MMRN), and Supply-Demand Ratio Net (SDRN). In their original form, prices are calculated in a single stage and the comprehensive analyses in the paper show that some members face increased cost. To solve this issue, the paper improves the methods by introducing the second stage in which the compensation methodology is defined for the distribution of savings which ensures that all community members gain benefits. Results investigate the value of inner technical flexibility of the prosumer (flexible preferences of the final consumer can reduce the cost from 3% up to 20 %). Moreover, incentives/penalties encourage the utilization of a flexible behavior to adjust the real-time consumption of prosumers' appliances to a predefined day-ahead schedule. This type of pricing results in a lower amount of benefits sharing in the community (the reduction of 18-47% in MMRN and 49-114% in SDRN compared to existing pricing) which makes this incentives/penalties pricing more preferable. The paper concludes that prosumers with an excess PV production would not benefit from the internal energy exchange in the community under BSMN due to free energy exchange between members.This work was supported in part by the Croatian Science Foundation (HRZZ) and Croatian Distribution System Operator (HEP ODS) HRZZ under project Active NeIghborhoods energy Markets pArTicipatION - ANIMATION (IP-2019-04-09164) through the project IMAGINE - Innovative Modelling and Laboratory Tested Solutions for Next Generation of Distribution Networks and by the Spanish Ministry of Economy, Industry and Competitiveness under Project ENE2017-83775-P, and in part by the European Research Council (ERC) under the European Union (EU) Horizon 2020 Research and Innovation Programme under Grant 755705