Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi-directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q-learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation-based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.© 2020 The Institution of Engineering and Technology. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)fi=vertaisarvioitu|en=peerReviewed

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this paper, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by Distribution Platform Optimizer (DPO). This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs’ and customers’ preferences. A bilevel programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system, which consists of DRAs’ cost and other electricity trading costs, is minimized. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and MonteCarlo Simulation method. In the lower-level problem, the electricity bill for customers is minimized for customers. The income from DR selling is maximized based on DR prices through secure communication of household energy management systems (HEMS) and DRA. To solve this convex and continuous bilevel problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.©2020 The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Stochastic Wind Power Generation Planning in Liberalised Electricity Markets within a Heterogeneous Landscape

Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres

Dynamic retail market tariff design for an electricity aggregator using reinforcement learning

The role of retailers, as energy providers for end-users, in restructured retail electricity markets becomes substantial. The increasing share of distributed energy resources and electrification in different sectors bring several challenges to retailers. Among these challenges, procuring electricity and maintaining system reliability during peak times induce high costs to retailers. Therefore, they need to accurately predict customers' demand to participate in the wholesale market and develop proper tariff mechanisms considering other retailers' behavior to maximize their profit. This paper develops the design of an autonomous retailer in which a Sequence-to-Sequence (Seq2Seq) algorithm is employed to predict customers' net demand. Furthermore, using Reinforcement Learning (RL), the proposed retailer designs tariff mechanisms based on other retailers' behavior and customers' load profiles. The proposed design of the retailer is evaluated on a retail market simulation platform called Power TAC, in which autonomous retailers compete in retail, wholesale, and balancing markets to maximize their profits. The results show the accuracy of the proposed load prediction method compared with other methods and successful profit growth with a drop in fixed costs and balancing costs

Local Flexibility Markets and Business Models

Current energy systems are experiencing a transformation led by incentives to reduce greenhouse gas emissions and increase the share of renewable energy sources (RES). This way, the integration of RES into energy systems is one of core issues. However, only depending on grid investments to deal with increasing loads and integration of RES is not the way to tackle this issue, because it would be too costly. Flexibility is defined as the change of energy generation or consumption patterns in response to a specific signal. This flexibility is then offered as a service to support actors in the energy system. Local flexibility markets are identified as platforms that coordinate and provide flexible assets. This chapter aims to provide an overview of local flexibility markets and their business models. This chapter analyzes the proposed local flexibility market designs in Europe, and discusses on their drawbacks and barriers, and how they can be improved.©2023 Springer. This is a post-peer-review, pre-copyedit version of an article published in Trading in Local Energy Markets and Energy Communities: Concepts, Structures and Technologies. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-21402-8fi=vertaisarvioitu|en=peerReviewed

A Review of Smart Cities Based on the Internet of Things Concept

With the expansion of smart meters, like the Advanced Metering Infrastructure (AMI), and the Internet of Things (IoT), each smart city is equipped with various kinds of electronic devices. Therefore, equipment and technologies enable us to be smarter and make various aspects of smart cities more accessible and applicable. The goal of the current paper is to provide an inclusive review on the concept of the smart city besides their different applications, benefits, and advantages. In addition, most of the possible IoT technologies are introduced, and their capabilities to merge into and apply to the different parts of smart cities are discussed. The potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper. Meanwhile, some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed

Stochastic demand side management in European zonal price market

In this paper, demand-side management (DSM) is performed through demand response aggregators (DRAs) in an uncertain environment within zonal price market framework. The proposed scheme aims to allow cross-border electricity trading and optimize interconnections usage as well as to obtain optimum DR volume from the perspective of the Market Coupling Operator (MCO). The market consists of several zonal price markets as Nominated Electricity Market Operators (NEMO) who run their day-ahead and balancing market internally and communicate the information to the MCO to provide the cooperation with other NEMOs. To this end, a stochastic two-stage model is formulated in which the total operation cost from MCO's viewpoint is minimized. Accordingly, the model aims to consider day-ahead decisions in the first stage and balancing decisions in the second stage. Furthermore, the intermittent nature of renewable sources generation is handled by scenario generation with Monte-Carlo Simulation (MCS) method. NEMOs are physically connected as radial network. Therefore, all relative network constraints are taken into account as a linear power flow for radial networks. The results of the implementation of the proposed model demonstrate the effectiveness of various DR biddings on hourly DR volume, hourly DR cost and power exchange between different NEMOS.fi=vertaisarvioitu|en=peerReviewed