In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. \n \nThis thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions.
Uzma Amin, M. J. Hossain, Edstan Fernandez, Khizir Mahmud · 5 authors
The following topics are dealt with: power engineering computing; power grids; optimisation; distributed power generation; learning (artificial intelligence); power markets; neural nets; demand side management; evolutionary computation; power generation economics.
This paper studies the multi-to-multiple energy trading mode and quotation strategy based on Block-chain intelligent contract and continuous auction mechanism. When distributed power generation entities and power consumers exist in Many-to-many form, buyers and sellers can be in the trading cycle at any time. The quotation is submitted to the smart contract address, and the quotation is continuously adjusted according to the transaction result disclosed by the smart contract. Once the price is matched, the transaction can be completed, and the transaction settlement is realized through the Block-chain intelligent contract. The IEEE13 node power distribution system is taken as an example to verify the effectiveness and feasibility of the proposed method.
The renewable energy industry represents a very dynamic sector, connected with other economic sectors. The trends in this field are numerous and various. The setting of EU targets for greenhouse gas emission reduction is a policy at European level. The huge amount of data calls for the use of complex data processing equipment and systems, so now we can talk about the smart grid, decentralization, the Energy Management System and the Energy storage system. Business development requires very large investments and various financing methods, but especially well-detailed and reasoned business models that can be adapted to local conditions. There are changes at the level of HR through the creation of new specific jobs, as well as continuous training for specialists. The main objective of this chapter is to present the major trends related to the renewable energy sector and their impact on the economic development and on the environment. This chapter presents 18 main trends identified by the authors and analyzed in the renewable energy field. Each of these trends follows in a brief overview the actual situation and the future perspective.
Distribution system operators (DSOs) are interested in demand side participation programs as an efficient and secure resource to manage electricity supply and demand. However, it is usually difficult for DSOs to aggregate demand response of large/small consumers. Thus, in some electricity markets, an entity called an aggregator is defined to aggregate the load response of consumers. In this paper a bilevel scheduling model is proposed to determine the long-term optimal contract price between the DSO and aggregator for executing direct load control in smart distribution systems. The DSO and aggregator are considered as two different agents with individual objectives in the proposed bilevel scheduling model. On the one hand, the aggregator maximizes its profit by bidding load reduction of the large consumers to the DSO by executing a direct load control (DLC) mechanism, and on the other hand, the DSO tries to minimize its overall cost to supply all consumers. The DSO has two options to follow the variation of its consumers' demand: purchasing energy from the electricity market and executing DLC programs. The bilevel programming formulation is transferred into an equivalent single level programming problem using its Karush-Kuhn-Tucker optimality conditions. Moreover, the uncertainties of the electricity market price, demand of consumers, and generation of a wind power plant are modeled via point estimate method. Two typical case studies are implemented to demonstrate the effectiveness of the proposed scheduling model.
The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process.
Pietro Danzi, Sarah Hambridge, Čedomir Stefanović, Petar Popovski
In the power grid, the Balance Responsible Parties (BRPs) purchase energy based on a forecast of the user consumption. The forecasts are imperfect, and the corrections of their real-time deviations are managed by a System Operator (SO), which charges the BRPs for the procured imbalances. Flexible consumers, associated with a BRP, can be involved in a demand response (DR) program to reduce the imbalance costs. However, running the DR program requires the BRP to invest resources in the infrastructure and increases its operating costs. To limit the intervention of BRP, we implement the DR via a blockchain smart contract. Moreover, to reduce the delay of publication of the imbalance price, caused by the inefficient accounting process of the current balancing markets, a second blockchain is adopted at the SO layer, procuring a fast and auditable credit settlements. The feasibility of the proposed architecture is evaluated over an Ethereum blockchain platform. The results show that block chains can enable a high automation of the balancing market, by providing (i) the implementation of aggregators with low operating cost and (ii) the timely and transparent access to the balancing information, thus fostering new business models for the BRPs.
Sanal para sistemi bitcoin ve altcoins olarak tanımlanan türevleri mevcut para politikasını ve para sistemlerini değişim ve dönüşüme zorlayacak bir trendin içinde oldukları gözlenmektedir. Genel olarak kripto para olarak tanımlanan bu sistem elektronik ortamda oluşturulabilen ve nakit benzeri bir ödeme aracı sisteminden ibarettir. Bu sistemin mevcut para ve ödeme araçlarına alternatif ve hatta geleneksel para teori ve uygulamalarının yerini alacağı yönünde bir gelişim süreci içinde olması bu sisteme odaklanmayı zorunlu hale getirmektedir. Bu makalede bitcoin ve benzeri kripto paraların yapıları, işlevleri ve mevcut para sistemi içerisindeki yeri ve önemi karşılaştırmalı ve analitik bir analizle değerlendirilmiştir.
The deployment of smart grids has facilitated the integration of a variety of investor assets into power distribution systems, giving rise to the consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This paper proposes a novel incentive-based distribution system expansion planning model that enables an LDC and distributed generation (DG) investors to work in a collaborative way for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program based on long-term contracts, which would encourage DG investors to integrate their projects at specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors' projects. To derive appropriate incentives for each project, the model enforces several economic metrics including internal rate of return, profit investment ratio, and discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. Several linearization approaches are applied to convert the proposed model into an MILP model. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Case study results have demonstrated the value of the proposed model.
Crowdsourcing relies on people's contributions to meet product- or system-level objectives. Crowdsourcing-based methods have been implemented in various cyber-physical systems and realtime markets. This paper explores a framework for Crowdsourced Energy Systems (CES), where small-scale energy generation or energy trading is crowdsourced from distributed energy resources, electric vehicles, and shapable loads. The merits/pillars of energy crowdsourcing are discussed. Then, an operational model for CESs in distribution networks with different types of crowdsourcees is proposed. The model yields a market equilibrium depicting traditional and distributed generator and load setpoints. Given these setpoints, crowdsourcing incentives are designed to steer crowdsourcees to the equilibrium. As the number of crowdsourcees and energy trading transactions scales up, a secure energy trading platform is required. To that end, the presented framework is integrated with a lightweight Blockchain implementation and smart contracts. Numerical tests are provided to showcase the overall implementation.
We compare two Demand Side Management (DSM) mechanisms, introduced\nrespectively by Mohsenian-Rad et al (2010) and Baharlouei et al (2012), in\nterms of efficiency and fairness. Each mechanism defines a game where the\nconsumers optimize their flexible consumption to reduce their electricity\nbills. Mohsenian-Rad et al propose a daily mechanism for which they prove the\nsocial optimality. Baharlouei et al propose a hourly billing mechanism for\nwhich we give theoretical results: we prove the uniqueness of an equilibrium in\nthe associated game and give an upper bound on its price of anarchy. We\nevaluate numerically the two mechanisms, using real consumption data from Pecan\nStreet Inc. The simulations show that the equilibrium reached with the hourly\nmechanism is socially optimal up to 0.1%, and that it achieves an important\nfairness property according to a quantitative indicator we define. We observe\nthat the two DSM mechanisms avoid the synchronization effect induced by non-\ngame theoretic mechanisms, e.g. Peak/OffPeak hours contracts.\n
Jun 1, 2017·2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Eleonora Riva Sanseverino, Maria Luisa Di Silvestre, Pierluigi Gallo, Gaetano Zizzo · 5 authors
In recent years novel models for energy distribution appeared and islanded microgrids quest for new ways to exchange energy between consumers and producers without the need of central authorities. The blockchain mechanism has emerged as a distributed solution for recording energy transactions in power systems. The blockchain has been used to permit users bartering and selling energy and to keep track of such exchanges without exposing them to tampering. In this work, we consider a novel application of the blockchain in islanded microgrids that includes also annotating energy losses caused by energy transactions, in order to have a more realistic matching between the physical status of the energy grid and the consequent costs attributed to users. To validate our novel use of the blockchain, we carried out simulated experiments for an exemplary islanded microgrid, in which 3 main generators supply 6 load nodes. This validates the compatibility of this new cost attribution model with the supporting physical infrastructure. Preliminary results demonstrate that the superposition of energy transactions in a microgrid changes the distribution of losses in all paths, eventually due to the large reactive flows created by PV systems.
A coordinated trading process is proposed as a design for an electricity market with significant uncertainty, perhaps from renewables. In this process, groups of agents propose to the system operator (SO) a contingent buy and sell trade that is balanced, i.e. the sum of demand bids and the sum of supply bids are equal. The SO accepts the proposed trade if no network constraint is violated or curtails it until no violation occurs. Each proposed trade is accepted or curtailed as it is presented. The SO also provides guidance to help future proposed trades meet network constraints. The SO does not set prices, and there is no requirement that different trades occur simultaneously or clear at uniform prices. Indeed, there is no price-setting mechanism. However, if participants exploit opportunities for gain, the trading process will lead to an efficient allocation of energy and to the discovery of locational marginal prices (LMPs). The great flexibility in the proposed trading process and the low communication and control burden on the SO may make the process suitable for coordinating producers and consumers in the distribution system.
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation. A large variety of STLF models have been developed that trade off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: mutual information based feature selection; and enhanced differential evolution algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network. Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing bilevel forecast strategy by 52.38%.
Biling Zhang, Chunxiao Jiang, Jung-Lang Yu, Zhu Han
Direct trading is a promising approach to simultaneously achieve trading benefits and reduce transmission line losses in smart grid. However, due to the selfish nature, small-scale electricity suppliers (SESs) and electricity consumers (ECs) will not participate direct trading if the trading does not bring them benefits. Therefore, how to provide proper economic incentives for these two parties to take part in direct trading is an essential issue. Nevertheless, the asymmetry trading information between them makes the problem challenging. In this paper, we propose a contract-based direct trading framework to tackle this challenge, in which the decision making process of ECs and SESs in the presence of asymmetric information is modeled as a contract game. In the proposed game, the EC designs a contract which contains its trading strategies toward all types of SESs. Through the contract, the EC not only attracts SESs to sell electricity but also maximizes its own revenue. The SESs, on the other hand, get maximal benefits if they truthfully select the contract items of their own types. We derive theoretically the optimal contract for the short-term market where the supply of SESs is deterministic. Then we extend our study to the long-term market where the supply of SESs is encountering significant uncertainty. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.
This paper proposes a novel risky bilateral contract for trading distributed wind energy with flexible load in smart microgrid. Due to the randomness of wind energy production, wind energy can barely be traded at a pre-determined quantity. While in smart grid, load demand of electricity consumer can be easily adjusted (reduced or shifted) without decreasing its satisfaction, which can be utilized for integration of distributed wind energy. As a result, the wind power producer benefits from fewer penalties caused by less deviation between real-time traded quantity and pre-offered one. The electricity consumer, on the other hand, benefits from a decreased electricity bills without reducing its satisfaction. We take into account distributed wind energy, three categories of household appliances, and batteries. The Nash bargaining theory is used to determine the price of risky bilateral contract. Case studies are then conducted to demonstrate the efficiency of the risky bilateral contract.
Spyros Giannelos, Ioannis Konstantelos, Goran Štrbac
Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies.