Iacopo Savelli, Hanumantha Rao Bokkisam, Paul Cuffe, Thomas Morstyn
The large deployment of renewable generation required to reach net-zero carbon emission requires significant investments in transmission network infrastructure to reduce grid congestion, as well as costly investments in dispatchable assets to manage the intermittency of renewable energy. The provision of flexibility services to system operators represents an additional method that could help solve these issues. However, this requires the engagement of a large number of small users, such as households and small commercial firms, that usually cannot directly participate in electricity markets due to their limited size. Blockchain technologies leveraging smart contracts can provide an autonomous, cost-saving and transparent tool to help engage these users in the provision of flexibility services to the grid. The aim of this paper is to design a new market layer for the on-demand provision of flexibility services by using smart contracts fully integrated with existing national-scale electricity markets. We demonstrate how this model can co-exist with the current electricity market architecture in Great Britain (GB), providing a whole-system least-cost solution to solve grid congestion and energy imbalances. Simulations based on a high-fidelity network of GB highlight the potential benefit that the proposed approach could create at the national scale.
Anu G Kumar, M R Sindhu, Vivek Mohan, Rekha Viswanathan · 5 authors
Rooftop solar PV in India has seen good progress in the Commercial and industrial sectors, but the progress in the domestic sector is relatively slow due to the high initial installation cost. Thus, there arises the need for good market models for Rooftop Solar (RTS) implementation. This paper conducts a comparative study of workable RTS market models by employing the discounted cash flow method, as per the recent regulatory guidelines. Market models are formulated and tested for a typical residential high-rise apartment complex in India comprising 15 storied buildings with a combined maximum demand of 180kVA. The results suggest that the centralized community RTS model of 80kWp capacity with upfront financing is suitable when compared to the decentralized individual model, as it has the lowest levelized cost of 3.39 ₹/kWh and a payback period of 5.5 years. With the federal subsidy, the prosumer levelized cost reduces to 2.06 ₹/kWh with a payback period of 3.3 years. Thus grid parity is achieved for all tariff tier rates. With adaptive staggering strategy, this scheme is validated to be more attractive for the urban residential microgrids, as the solar installation of 80kWp and its cost can be staggered and even reduced over the planning period. Hence capital installation and operation costs can be distributed over the stipulated time interval. The study result gives RTS stakeholders insight into selecting the most cost-effective market model to suit their requirements. Financial analysis of the proposed models provides input to the customers, developers, and policymakers to assess the financial merit of adopting the suitable business model for RTS development. The proposed analysis can be replicated for high-rise residential buildings, especially in cities with high electricity tariffs. With time, a decrease in solar PV installation price and an increase in grid price are expected; hence, the overall investment cost gets reduced and staggered.
Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources. In recent years, one of the fastest-growing flexible energy demands has been proof-of-work-based cryptocurrency mining facilities. Due to their competitive ramping capabilities and demonstrated flexibility, such fast-responding loads are capable of participating in frequency regulation services for the grid while simultaneously increasing their own operational revenue. In this paper, we investigate the physical and economic viability of employing cryptocurrency mining facilities to provide frequency regulation in large power systems. We quantify mining facilities' operational profit, and propose a decision-making framework to explore their optimal participation strategy and account for the most influential factors. We employ real-world ERCOT ancillary services data in our case study to investigate the conditions under which provision of frequency regulation in the Texas grid is profitable. We also perform transient level simulations using a synthetic Texas grid to demonstrate the competitiveness of mining facilities at frequency regulation provision.
Younes Zahraoui, Tarmo Korõtko, Argo Rosin, Hannes Agabus
Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.
Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.
Valeri Mladenov, Veselin Chobanov, Thong Vu Van, Pencho Zlatev
The architecture, characteristics, and elements of Blockchain technology used to create a peer-to-peer energy trading platform are outlined in the paper. Here, we discussed the benefits of Blockchain and distributed ledger technology (DLT) for energy trading applications and how they assist the expanding decentralization and democratization ideologies in the energy industry.
With the popularization of distributed energy resources, residents are able to trade electricity with each other as prosumers, promoting the emergence of community electricity markets with double auctions. Since these markets are small in scale, there is typically no authoritative market operating organization. Moreover, for the double auction market to last, budget deficits should not occur. To address these issues, this paper develops a blockchain-based trading framework for community electricity markets. A hierarchical architecture is established including a trading layer that supports prosumers and an incentive layer that encourages miners to maintain the blockchain system. Moreover, smart contracts are designed for prosumers and market operations. To resolve the budget deficit issue from the Vickrey-Clarke-Groves (VCG) mechanism with double auctions, an improved VCG double auction mechanism is developed with budget balance. Moreover, the improved mechanism retains the individual rationality and truthfulness properties of the original VCG mechanism. In addition, the possible budget surplus can be used as rewards for miners. A prototype is developed using the Ethereum platform. Case studies demonstrate the performance of the system and the budget balance of the improved mechanism.
Abstract Due to the growing number of Distributed Energy Resources and new electrical loads at the sectoral contact points, novel organisational forms such as Local Energy Markets arise to deal with increasing complexity in the energy system. However, these markets are radically different from traditional energy markets, as they often allow individual prosumers to trade with each other via a peer‐to‐peer scheme. To guarantee tamper‐proof settlement, an increasing number of these markets feature a distributed ledger technology. This paper analyses different design variants of peer‐to‐peer markets, focusing specifically on the allocation mechanism under network constraints as these mechanisms constitute the core component of a market design. We assess these designs concerning user acceptance, economic performance, practicability, and their ability to relieve grid congestion. Further key performance indicators also cover communal revenues or welfare distribution. For this purpose, we developed an agent‐based simulation framework, which builds on data from three German reference municipalities derived from a novel clustering approach. Besides a consolidated presentation of the results, we highlight current implementation obstacles and identify promising concepts for further research.
Electric power systems are transitioning towards a decentralized paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources. This paper proposes a novel Blockchain based peer-to-peer trading architecture which integrates negotiation-based auction and pricing mechanisms in local electricity markets, through automating, standardizing, and self-enforcing trading procedures using smart contracts. The negotiation of the volume and price of the peer-to-peer electricity trading among prosumers is modeled as a cooperative game, and the interaction between a retailer and its ensemble of prosumers is modeled as a Stackelberg game. The flexibility provision from residential heating systems is incorporated into the energy scheduling of prosumers. Case studies demonstrate that the proposed architecture in local electricity markets helps improve local energy balance. Flexibility from the residential heating systems enables prosumers to be more responsive to the variation of retail electricity prices. The proposed model reduces 41.24% of average daily electricity costs for individual prosumers or consumers compared to the case without the peer-to-peer electricity trading.
Valeri Mladenov, Veselin Chobanov, George Serițan, Radu Porumb · 9 authors
The paper’s main objective is to demonstrate the trading and flexibility of services amongst TSOs, DSOs, and Prosumers in a transparent, secure, and cost-effective manner using Blockchain-based TSO-DSO flexibility marketplace (EFLEX). The aim is to look for ways to help DSOs/TSOs be more flexible and more directly engaged in managing energy flows on the network. EFLEX will streamline the needs of both TSO and DSO on the same platform. Based on the paper’s proposed services, the pilot service demonstration will be carried out in Bulgaria and Romania, and the main focus will be on congestion management, TSO-DSO Coordination, and Marketplace. The proposed objective is achieved by using Blockchain-based smart contracts and distributed ledger technology.
Marco Galici, Mario Mureddu, Emilio Ghiani, Fabrizio Pilo
The progressive development of local energy communities as well as the growing interesting in Machine Learning approaches for the energy sector are leading to deep changes in to Power Systems sector. In this evolution scenario, the concept of local energy markets would lead to new operations and management approaches. It is well-established that markets based on distributed ledgers are suitable to the system requirements rather than centralized approaches. This study aims at introducing an agent-based model for simulating participants' offering mechanism. In this way the market users' behaviours are simulated in a realistic manner. This paper improves and enhances the results obtained from previous authors study, where a decentralized version of the common genetic algorithm was presented. The proposed agent-based framework has been implemented on a Real Time Digital Simulator, capable of inspect the grid dynamics. Finally, in order to reproduce participants behaviour, IoT smart devices was implemented.
A large number of grid-connected distributed photovoltaic and wind power generation projects bring challenges to traditional electricity market trading. A distributed power trading model based on blockchain technology is designed in this paper, and the transaction process is introduced in detail. The transaction is established in an annual cycle and settled monthly. The trading model adopts the average price of the sellers' and buyers' quotation price by matching, and the matching transaction takes into account the influence of the differences in transmission prices between different buyers and sellers. The matching transaction mechanism and the monthly transaction settlement mechanism are elaborated. A distributed power trading system based on blockchain technology is developed on the Ethereum platform. Combined with a distributed power trading example, the feasibility of the trading system for distributed power trading is verified.
The promising power-to-gas (P2G) technology makes it possible for wind farms to absorb carbon and trade in multiple energy markets. Considering the remoteness of wind farms equipped with P2G systems and the isolation of different energy markets, the scheduling process may suffer from inefficient coordination and unstable information. An automated scheduling approach is thus proposed. Firstly, an automated scheduling framework enabled by smart contract is established for reliable coordination between wind farms and multiple energy markets. Considering the limited logic complexity and insufficient calculation of smart contracts, an off-chain procedure as a workaround is proposed to avoid complex on-chain solutions. Next, a non-linear model of the P2G system is developed to enhance the accuracy of scheduling results. The scheduling strategy takes into account not only the revenues from multiple energy trades, but also the penalties for violating contract items in smart contracts. Then, the implementation of smart contracts under a blockchain environment is presented with multiple participants, including voting in an agreed scheduling result as the plan. Finally, the case study is conducted in a typical two-stage scheduling process—i.e., day-ahead and real-time scheduling—and the results verify the efficiency of the proposed approach.
Due to the increasing penetration of renewable energies, the energy imbalance market (EIM) is proposed to better facilitate the real time supply demand balance in the power system, by rewarding the market participants with better forecasts for the market conditions (i.e., the mismatch in the system). Together with many other financial instruments in the electricity sector, EIM calls for the market participants to strive for improving their forecast abilities. This increases the need for a data market in place. However, data market, compared with conventional commodity markets, has numerous unique impediments, such as distrust and data mutability issues. To tackle these challenges, we design blockchain enabled data transmission for centralized and decentralized EIM, respectively. We submit that although decentralized market is often a trade off between autonomy and market efficiency, there are conditions when decentralized market and centralized EIM achieve the same efficiency. Numerical studies further suggest, even when the conditions are violated, the efficiency loss in the decentralized EIM is still acceptable.
Baraa Mohandes, Mohamed Shawky El Moursi, Nikos Hatziargyriou, Sameh El Khatib
This article proposes a DR program characterized by a novel compensation scheme. The proposed scheme recognizes the different characteristics of curtailment, such as the total length of curtailments within a window of time, or the number of separate curtailment events (i.e., curtailment startup), and compensates the end-user accordingly. The proposed compensation scheme features a piece-wise reward function comprised of two intervals. DR participants receive a onetime reward upfront when they enroll in the DR program and accept a set of predefined curtailment aspects. Curtailment aspects in excess of the agreed quantities are rewarded at a linear rate. This design is tailored to appeal to residential DR participants, and aims to secure sufficient flexibility at minimum cost. The parameters of the smart contract are optimized such that the system's social welfare is maximized. The optimization problem is modeled as a mixed-integer linear program. Consequently, this article updates the unit-commitment (UC) formulation with the commitment aspects of DR units. The proposed extension to the UC problem considers the critical aspects of DR participation, such as: the total length of interruptions within a window, the frequency of interruptions within a time-window irrespective of their length, and the net energy deviation from the original load profile. Deployment of the smart DR contract in the unit dispatch problem requires translating DR participants' characteristics to their equivalent aspects in conventional thermal generators, such as minimum up time, minimum down-time, start-up and shutdown costs. The obtained results demonstrate significant improvement in social welfare, notable reduction of curtailed renewable energy and reduction in extreme ramping events of conventional generators.
This paper mainly proposes an intelligent transaction strategy of energy blockchain, aiming to safeguard the transmission of energy flow and information flow between users. Considering the diversity of power users, the power sellers were divided into reliable supply type (RST), low consumption type (LCT), environmental-friendly type (EFT), and affordable price type (APT), while power buyers were split into peak shifting type (PST) and stable demand type (SDT). Then, the comprehensive evaluation value (CEV) was calculated for each type of subjects. On this basis, the transaction strategy was optimized with the goal of maximizing the matching satisfaction of the two sides of the transaction. After that, the blockchain technology was introduced to the power matching decision-making process. The power transactions were made transparent and secure by the smart contract and consensus mechanism. Example analysis shows that our method improves the proportion of clean energies in power market, and ensures the stable supply, cost effectiveness, resource saving, and environmental-friendliness of the energy market.
This paper proposes and discusses the idea of using nascent blockchain hosted prediction markets as a decentralised crowd sourcing method for renewable energy forecasting. This method is further used as a risk management and hedging tool against volatility in weather variables they depend on. While existing approaches have been centralised by nature, with limited sources of input data and models, prediction markets allow anyone to participate in forecasting by betting on an outcome and earning profits for correct results. Since they have mercenary motivations, these participants are most likely to provide reliable and accurate information. Moreover, renewable energy producers can participate in these prediction markets to hedge against low-income periods due to poor weather conditions. This paper delivers a conceptual framework to exploit prediction markets in a blockchain platform with the aim of forecasting and hedging of renewable energy sources. The potential financial gain from applying this approach has been demonstrated through a case study for a typical small wind power producer.
Uzma Amin, M. J. Hossain, Wayes Tushar, Khizir Mahmud
Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.
Blockchain is a promising technology for local trading of the electricity. It has specific components, such as smart contracts, data ledger, consensus, and provides many benefits for both buyers and sellers because they are obtaining/generating electricity at better prices compared with the electricity from the public grid. This practice leads to a better integration of renewable energy sources, increasing the appetite for new local generation sources and storage facilities, transparency and trading opportunities for all market players. Grid operators also benefit from blockchain since the grid loading will be reduced as the grid does not have to transmit or distribute electricity from large power plants located far away from consumption place. In the end, the market players will benefit from reducing the grid loading and alleviating the congestions as onerous investment in grid infrastructure is avoided. In this paper, we will analyse the advantages of different electricity market mechanisms for trading and settlement. Several auction mechanisms such as pay-as-bid, uniform price, generalised second price or Vickrey-Clarke-Groves are taken into account as feasible options for local markets and peer-to-peer trading.
The increasing role of renewables, together with the escalation of digital technologies and the pressure for a more active role of consumers and prosumers, are the natural basis for the development of Local Electricity Markets (LEM). The goal of this paper is to contribute to the current debate on LEM, drafting several proposals about key issues to be considered in outlining the LEM business model and market design. We take advantage of the ongoing project "NEMoGrid", which aims at defining and validating a prototype of LEM by integrating local PVs generation into the grid and with a peer-to-peer trading scheme. Transactions are settled on the Ethereum blockchain and the LEM is validated through onsite tests in Switzerland. Such tests are still running, therefore we use preliminary findings to make our suggestions, also highlighting several caveats and policy complexities. Keywords: local energy markets, peer-to-peer, renewable energy sources, electricity market design, electricity business models.