This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous dayâs bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the programâs effectiveness in reducing both net energy costs and peak load on the electricity grid.
The advancement and proliferation of digitalization and communication infrastructure have facilitated the rise of real-time bidding markets in smart grids. In these dynamic markets, energy distribution companies and power-generating companies interact to establish energy exchange contracts based on offered prices. However, the fluctuation in power flow resulting from contract changes within the real-time bidding market introduces a potential vulnerability that malicious attackers can exploit to launch successful stealthy attacks. To enhance the smart grid resiliency against cyber-attack in the power market bidding environment, a new barrier-function adaptive finite-time trajectory tracking control is proposed in this paper. The developed controller is utilized to actively counteract and mitigate potential cyber-attacks to ensure their rejection and prevention. The stability analysis convincingly demonstrates the rapid convergence of system states within a finite time frame, empowering the system to effectively reject cyber-attacks in real-time. Test results of an IEEE test systems, considering governor dead bound nonlinearity and communication time delay are presented and compared with those obtained from other methods to ensure and demonstrate the performance of proposed method. The Speedgoat real-time target machine, along with Simulink real-time, validates the effectiveness of the proposed method.
Meeting the targets of Sustainable Development Goal (SDG) 7, which focuses on ensuring access to affordable, reliable, sustainable, and modern energy for all, poses significant challenges. Overcoming these hurdles requires innovative solutions that can bridge the gap between current capabilities and future needs. Swarm electrification emerges as a promising concept that could accelerate progress towards achieving SDG 7 goals by leveraging the collective power of decentralized energy resources. This paper presents a literature review on swarm electrification and related insights from case studies. The study delves into the concept of swarm electrification, placing it within the context of the prevailing trends in the power system sector: decentralization, decarbonization, and digitalization. It examines the role of digital technologies in enhancing swarm electrification and categorizes application areas according to the phases of swarm electrification. Particular attention is given to the technologies underpinning Deep Digitalization, such as distributed ledger technology, notably blockchain, and artificial intelligence, with a focus on machine learning. These technologies play pivotal roles in advancing swarm electrification. The review demonstrates how deep digitalization can facilitate the improvement of swarm electrification and ultimately support the integration of bottom-up initiatives with top-down grid expansion efforts over time.
Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESSâs profitability compared to the removal of NM.
Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.
Kuo-Yang Wu, Tzu-Ching Tai, Bo-Hong Li, ChengâChien Kuo
Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive costâbenefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility ownersâ net income by 1.25 times compared to merely installing charging infrastructure.
Mateo D. Roig Greidanus, GabâSu Seo, Sudip K. Mazumder
This paper presents a unified multi-timescale control approach for a power system with distributed energy resources to achieve cyber-resilient operation. The proposed concept combines two cyber-resilient control methods: proactive and reactive methods. The proactive method uses a blockchain that ensures measurement and control data can be securely exchanged among grid assets and also derives control set points as a load-sharing supervisory control, with an embedded logic called chaincode. The proactive method ensures data integrity, but it inherits stochastic latency with significant standard deviation due to the nature of the distributed ledgers and security measures, leading to challenges in control. To overcome this trade-off, the reactive approach uses event-driven communication. For this approach, rather than communicating the complete data, a lightweight data packet is communicated in a peer-to-peer fashion. Therefore, it guarantees driving the system into a stable operation in case the proactive operation degrades with insufficient latency. To validate the concept, Hyperleger Fabric blockchain 2.2 is used to characterize the latency and is customized for an inverter control system in this study. Based on the use case, a stability analysis is presented to evaluate the impact of the variable delay and to identify the need for a reactive approach to mitigate the effects of a prolonged delay in the proactive method. A test bed with two hardware inverter prototypes and a custom blockchain programmed with the unified method is developed for validation. A set of hardware experimental results validates the methodology and demonstrates the inverter system operations achieving frequency recovery and load-sharing restoration based on the unified control method.
Julia Groza, Seyyed Ali Sadat, Koami Soulemane Hayibo, Joshua M. Pearce
To assist electric utilities to overcome limitations of centralized billing and encourage distributed production of solar photovoltaic (PV) electricity, this study designs and assesses a novel open-source autonomous virtual utility to monitor users and enable peer-to-peer trading. This study provides system design and software implementation of the concept using blockchain technology written in Solidity and Truffle. A set of smart contracts adds users to a system and monitors their demand, PV generation, and facilitates transactions between users on an hourly basis when one user has PV-generated excess electricity, and another has demand. Unit tests for each of the contractsâ methods are developed in Solidity, and data on gas usage and costs is collected. Once the contracts have been written and evaluated, a JavaScript simulation is developed to use the contracts on real load and PV generation data for one year on an hourly basis. The results of two case studies are quantified: 1) true peers, where all houses are prosumers with rooftop PV, and 2) intermittent transition case, where PV deployment and demand are more varied. The results found that with ten users in the system, the true peers case study resulted in an uneconomic number of exchanges, but the intermittent transition case study resulted in more than a factor of twenty increases in exchanges and net cost savings. The savings more than doubles for both cases when time of use pricing is in effect. The system utility increases with more variability of PV production across participating users and is recommended for utilities targeting increases in distributed generation during the energy transition.
Mohammad Seyfi, Mehdi Mehdinejad, Behanm Mohammadi-Ivatloo, Jamshid Aghaei
Power systems undergoes a massive change in power delivery and consumer-side production, e.g., penetration of renewable energy resources (RES) and reshaping of consumers to form prosumers, in which the Peer-to-Peer (P2P) energy trading markets are among the most promising solutions for handling these changes. In this paper, a fully decentralized smart contract-based P2P energy token trading market for active retailers and prosumers is presented. Active retailers in this market play as a connection between the local P2P market and upstream markets, which enables the participation of small-scale prosumers in the energy and ancillary markets. They can optimize their decision-making strategy to gain the most profit from energy markets. This model can first encourage retailers to participate in the local P2P energy token market, and consequently, the utilization of renewable energy resources in the power systems is facilitated. The simulation results showed the importance of the demand response program and the effectiveness of the DR program on the independence of the local P2P energy token trading market.
Through a digital platform, distributed generations can be managed intelligently to increase the overall efficacy of the distribution system. It was made possible by the growing integration of distributed generation with smart meters, Internet of Things, smart sensors, etc. Decentralized peer-to-peer (P2P) energy trading is a new concept and is encouraged by blockchain technology (BT) due to its transparency, security, and speedy transaction handling. This article expands on the P2P concept by creating a decentralized energy trading system to demonstrate the benefits of BT in providing a secure and efficient transaction platform for a community microgrid system containing consumers, prosumers, and renewable energy source (RES) owners. The supplyâdemand ratio method is used to determine the P2P selling and buying prices within the network based on the optimized allocations of the prosumers/RESs owners and consumers. This article highlights the participation of miners (validators) in the microgrid ecosystem, specifically local prosumers and RES owners. By actively participating in the energy trading, miners can enhance energy security, increase system resilience, and enjoy financial incentives. The suggested model designed on the Ethereum platform showcases effective energy management of microgrid system operation and increased security level through a step-by-step implementation process.
As the current global environment is deteriorating, distributed renewable energy is gradually becoming an important member of the energy internet. Blockchain, as a decentralized distributed ledger with decentralization, traceability and tamper-proof features, is an important way to achieve efficient consumption and multi-party supply of new energy. In this article, we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregatorsâ revenue, consider the degree of microgrid usersâ preference for electricity thus increasing usersâ reliance on the blockchain market, and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue. The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.
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.
Manuel Sivianes, J. M. Maestre, AscensiĂłn ZafraâCabeza, Carlos Bordons
In this article, a stochastic programming method is used to control a residential microgrid where possible disturbance realizations over a horizon are shaped into a tree structure with a given probability. This tree is used by model predictive controllers within a hierarchical and distributed scheme. The upper layer is characterized by a smart contract deployed in the blockchain, acting as a fully distributed coordinator that performs control tasks and collects and distributes relevant data. The bottom layer consists of individual agents that solve locally a reduced tree-based model predictive control (TBMPC) problem and interact with the smart contract to iteratively optimize the overall problem in a distributed fashion. The performance of the approach is demonstrated through several simulations in which various power-trading configurations are assessed.
The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid. Distributed energy resources (DERs), which can provide operating reserve to the grid, are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources. Small-scale DERs can be aggregated as a virtual power plant (VPP), which is eligible to bid in the operating reserve market. Since the DERs usually belong to different entities, it is important to investigate the VPP operation framework that coordinates the DERs in a trusted manner. In this paper, we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs. Considering the heterogeneity of various DERs, we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs. By considering the mismatch between actual available reserve capacity and the estimated value, the performance of VPP in the operating reserve market is improved. A hardware-based experimental system is developed, and numerical results are presented to demonstrate the effectiveness of the proposed framework.
Liaqat Ali, M. Imran Azim, Jan Peters, Nabin B. Ojha ¡ 10 authors
In this paper, a framework is proposed for integration of peer-to-peer (P2P) trading-based local energy market (LEM) with the blockchain technology. The proposed LEM model allows prosumers and consumers to trade electricity among each other ensuring the presence of the retailer and network utility â who are also essential parts of a P2P network. The P2P contracts settled between various prosumers and consumers are governed by mutually agreed upon smart contracts â which are then written in an Ethereum blockchain to record and store bidding history, P2P transactions, and settlements. An effective formulation is also presented to capture P2P trading quantities and prices among participating prosumers and consumers in a decentralised fashion with an appropriate analysis of financial viability. Finally, a case study is conducted in a real Australian context; in which the engagement of both prosumers and consumers are taken into account, and the performance of the proposed blockchain-enabled LEM is compared with business-as-usual (BAU) to demonstrate the model's superiority.
Liaqat Ali, M. Imran Azim, Jan Peters, Nabin B. Ojha ¡ 10 authors
This paper presents a local energy market (LEM) model to conduct peer-to-peer (P2P) energy trading between a number of participants by dint of the Ethereum-based blockchain technology. The proposed LEM mechanism is structured by considering relevant functional constraints while energy trading is arranged between several participants in the presence of other stakeholders including energy retailer and network operator. LEM participantsâ mutual bidding intended P2P trading, actual settlement, and final billing are executed using the smart contracts in Ethereum blockchain to record LEM transactions and related data in an unchangeable and distributed fashion. Lastly, a case study is performed in an Australian suburb with 300 LEM participants, and the simulation results are benchmarked with an existing business-as-usual (BAU)scenario. The simulation results outline that the formulated LEM mechanism 1) reduces the electricity cost of participants remarkably while improving their self-sufficiency, 2) minimises power grid export and import, and 3) retains income margins for the energy retailer and network operator.
Veerapandiyan Veerasamy, L. P. Mohasha Isuru Sampath, Shailendra Singh, Hung D. Nguyen ¡ 5 authors
This paper presents a blockchain-based decentralized frequency control of an islanded microgrid (MG) using a novel federated learning fractional order recurrent neural network (FL-FORNN). A self-adaptive proportional-integral-derivative (PID) controller is proposed using FL-FORNN to handle the uncertainties of power generation by the prosumers while participating in the trading of the islanded MG. In this work, the frequency oscillations during peer-to-peer (P2P) energy trading are regulated through the appropriate design of an adaptive controller and the implementation of a smart contract participation matrix (SCPM) for balancing the generation and consumer demand. A Proof-of-Authority (PoA) private blockchain-based SCPM has been developed to secure the contract among peers. The SCPM provides the power demand information from a consumer to a prosumer who participates in the P2P energy market. It also provides power reference to the prosumer distributed generation as a supplementary control, in addition to the secondary frequency control of the system. To study this, an islanded MG comprising four source nodes (prosumers) and three demand nodes (consumers) are tested with the implementation of the PoA-based blockchain framework. With its SCPM calculation running in the blockchain framework, the decentralized control of prosumers is implemented by interfacing the OPAL-RT with Raspberry Pi devices. The robustness of the proposed FL-FORNN controller has been verified with the FORNN tuned PID controller. Furthermore, a comparative analysis is made with the centralized scheme and traditional decentralized frequency control technique.
Abstract Distributed renewable energy supply (RES) is a new pattern for the transformation of power grids. As a characteristic case of RES, microgrids have an advantage in convenient operation. However, the energy management of microgrids remains as a major concern. With the emergence of the decentralized paradigm, blockchain potentially provides a reliable energy data metering and payment for the whole life cycle of energy management. In particular, the demand response (DR) in the microgrid can stimulate demanders to spontaneously manage their load consumption and maintain the balance of the energy trading market. To achieve optimal DR, a dynamic pricing strategy under the blockchain and gameâtheoretic approach is proposed. First, the blockchainâbased architecture is applied to ensure the reliability of energy data and lay a foundation of binding agreements for games. Then, the pricing mechanism under the cooperative game is formulated to optimize DR. Moreover, to help resolve the optimal response quantity and reduce the supply punishment of the RES providers, the DR requires accurate forecasting of the energy generation and consumption profiles. Therefore, an ensemble method Long ShortâTerm Gate Support (LSTGS) is designed to forecast the RES and load power for intelligent agent to make decision on effective energy scheduling and DR. Taking the classic distributed energy context as a case study, we demonstrate the effectiveness of our approach and show that it can achieve DR profits maximized and improve the stability of the energyâtrading market.
The concern for privacy and scalability has motivated a paradigm shift to decentralized energy management methods in microgrids. The absence of a central authority brings significant challenges to promote trusted collaboration and avoid collusion. To address these issues, this paper proposes a blockchain-empowered microgrid energy management framework, which adopts a novel consensus-based algorithm with a collusion prevention mechanism. Aiming at social welfare maximization, the energy management problem is formulated into a convex and decomposable form, which can be solved in a decentralized manner. To prevent the collusion between malicious agents, we propose a random information transmission mechanism empowered by the blockchain smart contract to replace the time-invariant communication topology. The consensus-based algorithm is extended to obtain the optimal solution of the energy management problem on the random and time-varying communication topology. We theoretically proved that the proposed algorithm converges to the global optimal solution with a probability of 1, without violating the physical constraints of individual agents. The effectiveness of the proposed method was validated by multiple experiments, both within the simulation environment and on a hardware system.
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.