Jun 1, 2017·2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Fabien Imbault, Marie Swiatek, Rodolphe De Beaufort, R. Plana
Energy systems are evolving towards a more decentralized model accommodate with heterogeneous but competitive energy sources and energy storage systems (ESS). This will enable peer to peer energy transactions through microgrids architectures. This paper explores the use of blockchain technology implemented on an Industrial operating system (Predix) for a use case of green certificates, demonstrated within an eco-district.
Pietro Danzi, Marko Angjelichinoski, Čedomir Stefanović, Petar Popovski
Residential microgrids (MGs) may host a large number of Distributed Energy Resources (DERs). The strategy that maximizes the revenue for each individual DER is the one in which the DER operates at capacity, injecting all available power into the grid. However, when the DER penetration is high and the consumption low, this strategy may lead to power surplus that causes voltage increase over recommended limits. In order to create incentives for the DER to operate below capacity, we propose a proportional-fairness control strategy in which (i) a subset of DERs decrease their own power output, sacrificing the individual revenue, and (ii) the DERs in the subset are dynamically selected based on the record of their control history. The trustworthy implementation of the scheme is carried out through a custom-designed blockchain mechanism that maintains a distributed database trusted by all DERs. In particular, the blockchain is used to stipulate and store a smart contract that enforces proportional fairness. The simulation results verify the potential of the proposed framework.
Pietro Danzi, Marko Angjelichinoski, Čedomir Stefanović, Petar Popovski
Residential microgrids (MGs) may host a large number of Distributed Energy\nResources (DERs). The strategy that maximizes the revenue for each individual\nDER is the one in which the DER operates at capacity, injecting all available\npower into the grid. However, when the DER penetration is high and the\nconsumption low, this strategy may lead to power surplus that causes voltage\nincrease over recommended limits. In order to create incentives for the DER to\noperate below capacity, we propose a proportional-fairness control strategy in\nwhich (i) a subset of DERs decrease their own power output, sacrificing the\nindividual revenue, and (ii) the DERs in the subset are dynamically selected\nbased on the record of their control history. The trustworthy implementation of\nthe scheme is carried out through a custom-designed blockchain mechanism that\nmaintains a distributed database trusted by all DERs. In particular, the\nblockchain is used to stipulate and store a smart contract that enforces\nproportional fairness. The simulation results verify the potential of the\nproposed framework.\n
In a Smart Grid environment the performance measure of the grid is calculated by considering the fact that how accurately and precisely a load forecasting (LF) is done. A true Load Forecasting is vital to make a current grid smarter and more reliable when it comes to its performance. Demand Response (DR) contracts is a type of program in smart grid where the customer is free to select a type of contract which is given by the utility and is one of the growing factor which affects the load forecasting results in the Smart Grid, therefore in order to do a complete evaluation of smart grid performance and to accomplish an accurate load forecasting results the different types of contracts should also be studied. The purpose of this study is to accomplish two goals. The first one is to develop a suitable model which can incorporate various factors that can affect the load forecasting results. The subsequent goal is to identify the impact of the demand response contracts on the load forecasting results. In the proposed study, Support Vector Machine-Regression (SVR) is selected as the base methodology to perform a Short — Term Load Forecasting (STLF) under smart grid environment.
Adam Hahn, Rajveer Singh, Chen‐Ching Liu, Sijie Chen
Transactive energy paradigms will enable the exchange of energy from a distributed set of prosumers. While prosumers have access to distributed energy resources, these resources are intermittently available. There is a need for distributed markets to enable the exchange of energy in transactive environments, however, the large number of potential prosumers introduces challenges in the establishment of trust between prosumers. Markets for transactive environments create other challenges, such as establishing clearing prices for energy and exchanging money between prosumers. Blockchains provide a unique technology to address this distributed trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This paper introduces a smart contract that implements a transactive energy auction that operates without the need for a trusted entitys oversight. The auction mechanism implements a Vickrey second price auction, which guarantees bidders will submit honest bids. The contract is implemented on transactive agents on the WSU campus interacting with a 72kW PV array and the Ethereum blockchain. The contract is then used to execute auctions based on the energy from the the PV array and simulated building loads to demonstrate the auctions operations.
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.
Vasiljevska Julija, Jochem Douw, Anna Mengolini, Igor Nikolić
EU Regulation / /EC concerning common rules for internal market in electricity calls upon % of EU electricity consumers to be equipped with smart metering systems by , provided that a positive economic assessment of all long-term costs and benefits to the market and the individual consumer is guaranteed. Understanding the impact that smart metering systems may have on the electricity stakeholders (consumers, distribution system operators, energy suppliers and the society at large) is important for faster and e ective deployment of such systems and of the innovative services they o er. For this purpose, in this paper an agentbased model is developed, where the electricity consumer behaviour due to di erent smart metering policies is simulated. Consumers are modelled as household agents having dynamic preferences on types of electricity contracts o ered by the supplier. Development of preferences depends on personal values, memory and attitudes, as well as the degree of interaction in a social network structure. We are interested in exploring possible di usion rates of smart metering enabled services under di erent policy interventions and the impact of this technological di usion on individual and societal performance indicators. In four simulation experiments and three intervention policies we observe the di usion of energy services and individual and societal performance indicators (electricity savings, CO 2 emissions savings, social welfare, consumers' comfort change), as well as consumers' satisfaction. From these results and based on expert validation, we conclude that providing the consumer with more options does not necessarily lead to higher consumer's satisfaction, or better societal performance. A good policy should be centred on e ective ways to tackle consumers concerns.
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.
I. INTRODUCTIONElectric utilities in the United States can no longer rely solely on producing and selling kilowatts to generate revenue. The challenges facing these companies today include: flattening electricity consumption, pressing resiliency and security concerns, and rising demand for distributed resources (DER). In addition to implementing standard system upgrades, utilities are being called to integrate decentralized assets, facilitate customer generation and use options, and invest in smarter grid technology-all while operating more efficiently and with less carbon output.These realities are fundamentally changing the way that utilities will be operated and regulated in the near future. Several states, like New York, Minnesota, Massachusetts, California, and Hawaii, are investigating how to prepare for and guide this evolution. At the forefront of these discussions are important questions over how utility business models and rate structures must change, as well as how tomorrow's electricity system will continue to deliver affordable, reliable, and universal service.Under-examined throughout this process, however, is the concern of how this grid evolution will impact the most vulnerable in our communities: ratepayers. Indeed, these changes raise a host of consumer protection issues from addressing stringent distributed financing rules1 and the landlord- tenant impediment to upgrades,2 to ensuring cost containment as smart metering enables new pricing structures.3 This article analyzes just one aspect of this multifaceted conundrum: how modern rate structure reforms will likely impact ratepayer assistance programs. The goal is to explain this problem and provide a preliminary set of policy solutions for industry members, stakeholders, and regulators. No single policy will provide the answer for most states. However, using some of the suggestions outlined in this article, in combination with an inclusive dialogue, we can better ensure a more just and equitable outcome for consumers in the electricity system of tomorrow.To that end, this article proceeds as follows: Part II describes the chronic energy burden weighing down households, and explores the various federal and state policies in place to lighten this load. Part III follows with a brief discussion of the current set of challenges prompting grid modernization efforts, and describes what implications those efforts present for ratepayers. With this background as context, Part IV lays out a series of policy approaches that can help regulators and reformers address these concerns and meet their intended objective: modernizing the electricity system while ensuring affordable service, universal access, and equal participation for all ratepayers.II. THE ENERGY BURDEN FACING LOW-INCOME HOUSEHOLDS AND THE CURRENT STATE OF ASSISTANCE PROGRAMSTo better understand the proposed grid reforms outlined in Part IV, it is necessary to first set out the status quo for households in the United States today. This Part discusses the primary metric by which affordability is often measured by utilities and regulators: the burden. The second subsection addresses federal and state-level policies and programs to help alleviate this burden.A. What Is the Energy Burden?According to the most recent data from the U.S. Census Bureau, 46.7 million people were in poverty in the United States in 2014-nearly 14.8% of the population. 4 But those numbers tell only part of the story. Generally, although each state and utility-run assistance program defines low-income differently, most peg eligibility to certain thresholds at or up to 200% of the federal poverty level (FPL)-which brings the total of people potentially eligible for assistance programs in the United States to around 106 million.5In the context of assistance, many programs look to not only a person's income in relation to the FPL but also his or her total energy burden- that is, the percentage of a customer's income spent on energy. …
Ke Zhang, Yuming Mao, Supeng Leng, Ming Zeng · 7 authors
Vehicle-to-Grid (V2G) is a promising paradigm to alleviate energy supply and demand imbalance of the grid. To further improve the power transmission efficiency, in this paper, we propose a cloudlet-based Vehicle-to-Vehicle (V2V) energy exchange framework. In the framework, the Energy Switch Center (ESC) serves as a trading broker, which purchases electricity from discharging vehicles and then resells it to the charging ones without energy transmission on the grid. The energy trading process is modeled in a contract theoretic approach. We derive the optimal feasible contracts which maximize the profit of the ESC. Furthermore, we systematically study the practical scenario where both the charging demand and renewable energy supplement are random variables, and propose a practical optimal contract-based electricity purchase scheme. Simulation results show that the proposed scheme can efficiently increase the profit of the ESC than the other mechanisms.
Cherrelle Eid, Paul Codani, Yannick Pérez, Javier Reneses · 5 authors
In many electric systems worldwide the penetration of Distributed Energy Resources (DER) at the distribution levels is increasing. This penetration brings in different challenges for electricity system management; however if the flexibility of those DER is well managed opportunities arise for coordination. At high voltage levels under responsibility of the system operator, trading mechanisms like contracts for ancillary services and balancing markets provide opportunities for economic efficient supply of system flexibility services. In a situation with smart metering and real-time management of distribution networks, similar arrangements could be enabled for medium- and low-voltage levels. This paper presents a review and classification of existing DER as flexibility providers and a breakdown of trading platforms for DER flexibility in electricity markets.
Nicolas Gensollen, Vincent Gauthier, Monique Becker, Michel Marot
In the context of the smart grid, we propose in this paper an algorithm that forms coalitions of agents, called prosumers, that both produce and consume. It is designed to be used by aggregators that aim at selling aggregated surplus of production of the prosumers they control. We rely on real weather data sampled across stations of a given territory in order to simulate realistic production and consumption patterns for each prosumer. This enables us to capture geographical correlations among the agents while preserving the diversity due to different behaviors. As aggregators are bound to the market operator by a contract, they seek to maximize their offer while minimizing their risk. The proposed graph-based algorithm takes the underlying correlation structure of the agents into account and outputs coalitions with both high productivity and low variability. We show that the resulting diversified coalitions are able to generate higher benefits on a constrained energy market, and are more resilient to random failures of the agents.
This paper analyses the heterogeneity of household consumer preferences for electricity service contracts in a smart grid context. Platform pricing strategies that could incentivise consumers to participate in a two-sided electricity platform market are discussed. The research is based on original data from a discrete choice experiment on electricity service contracts that was conducted with 1,892 electricity consumers in Great Britain in 2015. We estimate a flexible mixed logit model in willingness to pay space and exploit the results in posterior analysis. The findings suggest that while consumers are willing to pay for technical support services, they are likely to demand significant compensation to share their usage and personally identifying data and to participate in automated demand response programs involving remote monitoring and control of electricity usage. Cross-subsidisation of consumers combining appropriate participation payments with sharing of bill savings could incentivise participation of the number of consumers required to provide the optimal level of demand response. We also examine the preference heterogeneity to suggest how, by targeting customers with specific characteristics, smart electricity service providers could significantly reduce their customer acquisition costs.