Mohamed A. Mohamed, Seyedali Mirjalili, Udaya Dampage, Saleh H. Salmen · 6 authors
The impressive furtherance of communication technologies has exhorted industrial companies to link-up these developments with their own abilities with the target of efficiency enhancement through smart supervision and control. With this in mind, the blockchain platform is a prospective solution for merging communication technologies and industrial infrastructures, but there are several challenges. Such obstacles should be addressed to effectively adopt this technology. One of the most recent challenges relative to adopting blockchain technology is the energy consumption of miners. Thus, providing an accurate approach that addresses the underlying cause of the problem will carry weight in the future. This work addresses managing the energy consumption of miners by using the advantage of distributed generation resources (DGRs). Along the same vein, it appears that achieving the optimal solution requires executing the modified reconfirmation of DGRs and miners (indeed, mining pool systems) in the smart grid. In order to perform this task, this article utilizes the Intelligent Priority Selection (IPS) method since this method is up to snuff for corporative allocation. In order to find practical solutions for this problem, the uncertainty is also modeled as a credible index highly correlated with the load and generation. All in all, it can be said that the outcome of this research study can help researchers in the field of enhancement of social welfare by using the proposed technology.
T. Yuvaraj, K.R. Devabalaji, S. Srinivasan, Natarajan Prabaharan · 7 authors
The electric power distribution plays a crucial part in the power systems to maintain the power quality and stability of the system for loss mitigation and voltage regulation. The reactive power compensators like a capacitor, DG, and DSTATCOM play a vital role in power quality and stability improvement by providing proper reactive power in the distribution system. This article provides a critical review and comparative analysis of various compensating devices allocation to achieve power loss mitigation and voltage regulation in the radial distribution system (RDS). The Bat Algorithm (BA) and Voltage Stability Index (VSI) are utilized for determining the optimal size and site of the compensating devices in the RDS. Further, the application of Blockchain technology is utilized for voltage regulation of energy trading RDS. To show the effectiveness of the present study, an IEEE 33 test system is considered. All three compensators are implemented in IEEE 33 test system and compared with existing approaches. The present study is very much helpful to the Distribution Network Operators for selecting the suitable compensator in real-time applications.
Misagh Dehghani Ghotbabadi, Saeed Daneshvar Dehnavi, Hadi Fotoohabadi, Hasan Mehrjerdi · 5 authors
Abstract This paper tries to address the optimal operation of networked microgrid from the reliability perspective in a correlated atmosphere for the wind generators. The suggested approach performs based on unscented transformation in the form of a nonlinear projection and the heuristic method as the optimizer. The proposed structure is arranged as a complex constraint optimization problem with several targets seeing the varied objectives such as energy not supplied, system interruption frequency, system interruption duration and energy losses. Owing to the interrelated natural surroundings of multi‐microgrids, it is a necessity for the microgrids to let the each other access the operation info and with the central unit. In this situation, it is quite wise to provide a secured construction made of the blockchain for the assurance of the reliability and adequate security of data sharing in the microgrids. With the aim of validation of the proposed model, an IEEE standard system is considered and divided into four interrelated microgrids with one side connection to the main grid. The simulation results show the high capability of the proposed framework for enhancing the operation and reliability indices. Moreover, it is seen that almost 0.6% and 0.77% additional cost is imposed to the system in the deterministic framework in the first and second scenarios, respectively.
The grid connected photovoltaic (PV) power plants (PVPPs) are booming nowadays. The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. In order to stabilize the grid, either energy storage device - mainly batteries - or a power curtailment technique can be used. The additional cost on utilizing batteries make it not preferred solution, because it leads to a drop in the return on investment (ROI) of the project. A good alternative, is using a customized load (such as; cryptocurrency-based loads) which consumes the surplus energy. This paper investigating the usage of a customized load - cryptocurrency mining rig - to create an added value for the owner of the plant and increase the ROI of the project. These devices are widely used to perform the required calculations for validating the transactions on the network of the Blockchain. A comparison between the ROI of the mining rig and the battery have been conducted in this study. Based on this study the mining rig has superior ROI of 7.7% - in the case with the lowest ROI - compared to 4.5% for battery. Moreover, an improved controlling strategy is developed to combine both the battery and mining rig in the same system. The developed strategy is able to keep the profitability as high as possible during the fluctuation of the mining network.
This work presents the design and implementation of a blockchain system that enables the trustable transactive energy management for distributed energy resources (DERs). We model the interactions among DERs, including energy trading and flexible appliance scheduling, as a cost minimization problem. Considering the dispersed nature and diverse ownership of DERs, we develop a distributed algorithm to solve the optimization problem using the alternating direction method of multipliers (ADMM) method. Furthermore, we develop a blockchain system, on which we implement the proposed algorithm with the smart contract, to guarantee the transparency and correctness of the energy management. We prototype the blockchain in a small-scale test network and evaluate it through experiments using real-world data. The experimental results validate the feasibility and effectiveness of our design.
The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, \rv{brings} a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users' independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users' energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data-trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users' cost by up to 38.6% and reduces the overall system cost by 11.2%.
The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem's effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.
Mohamed Hamouda, Mohammed E. Nassar, M.M.A. Salama
Inter-connected Microgrids (IMGs) have emerged as a promising structure for future grids, offering resilience and independence in energy exchangeability with neighbours. To enable such interconnected structure, an interconnected market between individual Microgrids (MGs) participating via an agent (i.e., Energy Management System [EMS]) is required. Each agent is Self-Benefit-Driven (SBD), which means that it works in the best interests of its own MG. Therefore, energy trading is established to enhance these benefits. In this paper, a new strategy is proposed for IMG energy trading that considers SBD actions for MGs' agents, and a unique utility function for each MG is defined. The function includes import and/or export benefits for each MG. Furthermore, the definition of the utility also considers the MG's different objectives when importing versus exporting. A centralized Nash bargaining model is proposed for IMG energy trading to ensure fair settlements through a central entity (e.g., Distributed System Operator [DSO]). The proposed algorithm is developed using an adapted blockchain that enhances the security and transparency of the platform. The effectiveness of the proposed strategy is verified using a number of case studies.
D. Danalakshmi, R. Gopi, A. Hariharasudan, Iwona Otola · 5 authors
The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.
Nallapaneni Manoj Kumar, Aneesh A. Chand, Maria Malvoni, Kushal A. Prasad · 7 authors
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.
Abstract The global transition toward sustainable energy paradigms necessitates sophisticated, robust control mechanisms capable of managing the intermittent and stochastic nature of renewable energy sources (RES). This article investigates the implementation of microcontroller-based embedded systems as the foundational architecture for real-time renewable energy management. We analyze the evolution of control strategies—from passive, reactive monitoring to active, predictive, and intelligent power management—and evaluate the pivotal role of microcontrollers in optimizing the integration of solar and wind energy into domestic, industrial, and microgrid infrastructures. By examining the synergy between hardware constraints and software optimization, we demonstrate that microcontroller-based architectures, when coupled with advanced sensor arrays, significantly improve power efficiency, minimize harmonic distortion, reduce load losses, and enhance grid stability. This study serves as a comprehensive retrospection of the foundational innovations that defined energy management engineering between 2010 and 2019, providing a roadmap for the intelligent, decentralized grids of the future. Keywords: Microcontrollers, Renewable Energy, Embedded Systems, Smart Grids, Power Optimization, Solar Photovoltaic Systems, Energy Management Systems (EMS) 1.Introduction The increasing global demand for electricity, coupled with the urgent requirement to decarbonize energy production, has accelerated the adoption of renewable energy sources. However, the inherent intermittent output of wind and solar energy—dictated by weather patterns and diurnal cycles—presents significant challenges for grid reliability, frequency regulation, and power quality. Embedded systems, specifically microcontroller-based units, have emerged as the primary solution for the autonomous, real-time regulation of power distribution, voltage stabilization, and battery state-of-charge management. This paper reviews the foundational technological trends from 2010 to 2019, analyzing how these developments established the critical infrastructure for contemporary smart energy management. As the backbone of decentralized energy, microcontrollers have transitioned from simple monitoring tools to sophisticated edge-computing devices that enable autonomous grid-edge decision-making. By moving the "intelligence" of the grid closer to the source of generation and consumption, these embedded systems have effectively mitigated the negative impacts of power intermittency, enabling a shift from centralized, fossil-fuel-dependent architectures to resilient, distributed green energy networks. This transition has fundamentally altered the relationship between consumers and utility providers, transforming passive energy users into active "prosumers" who contribute to grid stabilization through localized, automated energy management, thereby increasing the overall elasticity of the modern power market. Furthermore, the standardization of these embedded interfaces has lowered the barrier to entry for small-scale developers, fostering a competitive ecosystem of innovative energy solutions that prioritize efficiency and local adaptability. This decentralization has, in turn, spurred advancements in micro-inverter technologies and energy storage systems (ESS), which rely heavily on low-latency microcontroller processing to perform critical tasks like phase synchronization and islanding detection, ensuring that distributed energy systems remain safely connected or gracefully disconnected during grid faults. The evolution of this field reflects a paradigm shift where grid-edge intelligence is no longer a luxury, but a necessity for surviving in a low-inertia energy environment. Ultimately, the integration of these microcontrollers into residential and industrial infrastructure serves as the catalyst for a more responsive grid capable of absorbing the volatility inherent in renewable energy generation. The result is a highly granular, responsive network where individual nodes contribute to the collective health and efficiency of the macro-grid. As these nodes learn to communicate their state and capacity, we witness the emergence of "Swarm Intelligence" in power distribution, where thousands of small, distributed controllers collectively act to stabilize a neighborhood-level grid against external disturbances. This distributed control architecture mimics biological systems, where localized interactions lead to emergent, system-wide stability, providing a robust buffer against the unpredictable nature of intermittent weather-based energy inputs. By decentralizing the control logic, the power network gains a level of self-healing and self-organization that centralized fossil-fuel plants could never achieve, turning the grid into a living, adaptive infrastructure. This transition towards self-optimizing neighborhood-scale energy systems represents the culmination of a decade of embedded innovation, where the aggregate behavior of micro-nodes replaces the rigid, top-down dispatch models of the previous century. 2. Microcontroller Roles in Energy Management Microcontrollers serve as the "brains" of modern energy harvesting and distribution systems. Research during the 2010s demonstrated that these units provide the high-speed computational power required to process complex sensor data in real-time, effectively bridging the gap between physical power components and software-defined control. Real-time Monitoring and Data Acquisition: Microcontrollers utilize Analog-to-Digital Converters (ADCs) to track vital parameters such as voltage, current, frequency, and environmental variables (temperature, solar irradiance, wind speed). This precise data capture allows for the identification of power fluctuations before they impact the grid. High-resolution sampling enables microcontrollers to perform spectral analysis, detecting deviations—such as voltage sags or frequency spikes—that could indicate impending component failure or grid instability. Furthermore, the integration of Non-Volatile Memory (NVM) allows for the logging of historical performance data, facilitating predictive maintenance and long-term efficiency analysis. This data-driven approach is crucial for minimizing operational downtime in remote renewable installations, where physical inspection is costly and logistically difficult. By analyzing trend patterns in historical data, these controllers can suggest preventative maintenance, ensuring the reliability of the system in harsh environmental conditions. The ability to monitor high-frequency harmonic content also provides insight into the degradation of power electronic components like IGBTs and capacitors, allowing for proactive component replacement before catastrophic failure occurs. This proactive monitoring extends the operational lifespan of power electronics, reducing the total cost of ownership for renewable assets. By aggregating this data into cloud-based dashboards, operators can derive actionable insights that optimize system performance across regional deployments. This data visibility fosters a transparent energy market where every kilowatt-hour is tracked, accounted for, and optimized for maximum yield. Furthermore, by utilizing edge-side analytics, microcontrollers can now flag anomalous consumption patterns that may indicate faulty grid-side infrastructure, acting as a secondary diagnostic layer for distribution utilities. This turns the humble meter into a sophisticated grid sensor, capable of localized grid-health reporting and load profiling that was previously impossible. Load Balancing and Dynamic Switching: By implementing adaptive logic, microcontrollers can dynamically switch between utility grid power and renewable storage (battery banks) based on real-time demand, tariff structures, and storage availability. This optimizes the utilization of self-generated power, reducing reliance on the main grid and minimizing electricity costs for the end-user. Sophisticated priority-based scheduling algorithms allow these controllers to manage residential or small-scale industrial loads, ensuring essential systems—such as medical equipment, refrigeration, or security systems—maintain power during grid fluctuations. This dynamic response prevents deep discharge of batteries, significantly extending their operational lifespan, and reduces the need for expensive, centralized peaker-plant power generation that usually relies on high-carbon fuel sources. In large-scale deployments, these controllers facilitate "load shedding" during peak demand, allowing for a more stable and balanced load across the entire local distribution circuit. Furthermore, by utilizing "time-of-use" pricing models programmed directly into the controller's logic, energy management systems can prioritize self-consumption when grid prices are highest, maximizing the economic viability of renewable investments for the consumer. This capability empowers users to actively shape their energy footprint, providing a tangible economic incentive for the deployment of renewable resources. By shifting non-essential loads—such as water heating, EV charging, or HVAC operation—to periods of high solar/wind production, prosumers effectively minimize their carbon footprint while simultaneously relieving the stress on the utility infrastructure. This creates a "demand-side flexibility" that grid operators can leverage to stabilize the network, turning consumers into active, paid participants in the grid's operational strategy, thus establishing a symbiotic economic relationship between the utility and the home. The microcontroller acts as the mediator in this transaction, autonomously making decisions that optimize the user's economic utility while aligning with the macro-grid's stability requirements. Maximum Power Point Tracking (MPPT): The implementation of advanced algorithms (such as Pe
Shivam Saxena, Hany E. Z. Farag, Hjalmar Turesson, Henry Kim
Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real‐world ADNs are executed to demonstrate the efficacy of the proposed system.
Gijs van Leeuwen, Tarek AlSkaif, Madeleine Gibescu, Wilfried van Sark
In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.
The optimal deployment of heterogeneous energy storage (HES), mainly consisting of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multienergy systems. Consequently, this article proposes a risk-averse method for HES deployment in a residential multienergy microgrid (RMEMG), considering the diverse uncertainties and multienergy demand-side management (DSM). Apart from the HES size and location planning, its optimal investment phase is also determined by maximizing the system equivalent daily profit (EDP) and minimizing the risk. To handle the system uncertainties from renewable energy sources, power demands, outdoor temperature, and residential hot water needs, the multistage adaptive stochastic optimization approach is utilized. Then, through the constraint linearization and stochastic scenario sampling, the original nonlinear deployment model is converted to a mixed-integer linear programming one and tested on an IEEE 33-bus distribution network based RMEMG. The effectiveness of the proposed method is verified by comparing it with the existing practices. The comparison results indicate that the proposed risk-averse deployment method can effectively increase the system EDP and more immune to the uncertainties. Besides, this method can be practically applied for the emerging RMEMGs, such as smart buildings, intelligent homes, etc., which get long-term DSM contracts.
Bogdan-Constantin Neagu, Ovidiu Ivanov, Gheorghe Grigoraş, Mihai Gavrilaș
A growing number of households benefit from the government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefitting from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (μG) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants’ locations, instantaneous active power demand, maximum daily energy demand and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.
Miguel Gayo-Abeleira, Carlos Santos, Francisco J. Rodríguez, Pedro Martı́n · 6 authors
Since the advent of the microgrid (MG) concept, almost two decades ago, the energy sector has evolved from a centralized operational approach to a distributed generation paradigm challenged by the increasing number of distributed energy resources (DERs) mainly based on renewable energy. This has encouraged new business models and management strategies looking for a balance between energy generation and consumption, and promoting an efficient utilization of energy resources within MGs and minimizing costs for the market participants. In this context, this paper introduces an efficient management strategy, which is aimed at obtaining a fair division of costs billed by the utilities, without relying on a centralized utility or MG aggregator, through the design of a local event-based energy market within the MG. This event-driven MG energy market operates with blockchain (BC) technology based on smart contracts for electricity transactions to both guarantee veracity and immutability of the data and automate the transactions. The event-based energy market approach focuses on two of the design limitations of BC, namely the amount of information to be stored and the computational burden, which are significantly reduced while maintaining a high level of performance. Furthermore, the prosumer data is obtained by using IEC 61850 standard-based commands within the BC framework. By doing so, the system is compatible with any device irrespective of the manufacturer implementing the IEC 61850 standard. The advantages of this management approach are considerable for: MG participants, in terms of financial benefits; the MG itself, as it can operate more independently from the main grid; and the grid since the MG becomes less unpredictable due to the internal energy exchanges. The proposed strategy is validated on an experimental setup employing low-cost devices.
Muhammad Fahad Zia, Mohamed Benbouzid, Elhoussin Elbouchikhi, S. M. Muyeen · 6 authors
Prosumer concept and digitilization offer the exciting potential of microgrid transactive energy systems at distribution level for reducing transmission losses, decreasing electric infrastructure expenditure, improving reliability, enhancing local energy use, and minimizing customers' electricity bills. Distributed energy resources, demand response, distributed ledger technologies, and local energy markets are integral parts of transaction energy system for emergence of decentralized smart grid system. Hence, this paper discusses transactive energy concept and proposes seven functional layers architecture for designing transactive energy system. The proposed architecture is compared with practical case study of Brooklyn microgrid. Moreover, this paper reviews the existing architectures and explains the widely known distributed ledger technologies (blockchain, directed acyclic graph, hashgraph, holochain, and tempo) alongwith their advantages and challenges. The local energy market concept is presented and critically analyzed for energy trade within a transactive energy system. This paper also reviews the potential and challenges of peer-to-peer and community-based energy markets. Proposed architecture and analytical review of distributed ledger technologies and local energy markets pave the way for advanced research and industrialization of transactive energy systems.
Xiaolin Fu, Hong Wang, Zhi-Jie Wang, Zhong Shi · 6 authors
This paper aims to study the problems of surplus interaction, poor real-time performance, and excessive processing of information in the micro-grid scheduling and decision-making process. Firstly, the micro-grid dual-loop mobile topology structure is designed by using the method of block-chain and multi-agent fusion, realizing the real-time update of the decision-making body. Secondly, on the basis of optimizing the decision-making body, a two-layer model of intelligent decision-making under the decentralized mechanism is established. Aiming at the upper model, based on the theory of block-chain consensus mechanism, this paper proposes an improved evolutionary game algorithm. The maximum risk-benefit in the decision-making process is the objective function, which realizes the evaluation and optimization of decision tasks. For the lower layer model, based on the block-chain distributed ledger theory, this paper proposes an improved hybrid game reinforcement learning algorithm, with the maximum controllable load participation as the objective function, and realizes the optimal configuration of distributed energy in the micro-grid. This paper reveals the rules of group intelligent decision making in micro-grid under multi-task. Finally, the effectiveness of the proposed algorithm is verified by using Beijing Jin-feng Energy Internet Park data.
The rapid development of distributed energy resources (DER) in the distribution grid calls for novel control and coordination solutions. Optimal management of DER will enable end-users to decrease their electricity costs and provide crucial services to grid operators. In this paper, a decentralized Optimal Power Flow (OPF) model is used to locally coordinate DER in distribution networks, while considering the network constraints, in a distributed, transparent and secure fashion. To achieve that, a consensus-based distributed optimization algorithm is developed using the general form Alternating Direction Method of Multipliers (ADMM). To enable transparent and verifiable management of the network, the paper provides a comprehensive procedure for the implementation of the decentralized OPF on a private blockchain-smart contracts platform. The performance of the proposed framework is tested using real data from a case study in a residential neighborhood in Amsterdam with different varieties of DER. The implementation procedure on a blockchain-smart contracts platform may be adopted in other problems that require a smart contract to act as a virtual aggregator.
Boyu Wang, Morteza Dabbaghjamanesh, Abdollah Kavousi‐Fard, Shahab Mehraeen
Power grid resilience, reliability, and sustainability can be improved significantly by decomposing the large grids into networked microgrids (NMGs). However, the optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose a secured stochastic energy management framework for NMGs based on the modified blockchain approach, utilizing the directed acyclic graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security and lower risks within the network, thus eliminating the financial fraud and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models, mainly due to the storage and high complexities of hash address calculations, this paper proposes a new modified blockchain technology based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. The unscented transform (UT) approach is employed to model the uncertainties of forecast error in hourly load demand, solar power output, and wind turbines power output. Finally, the proposed model is tested on an NMG system with four MGs, including two residential MGs (as the noncrucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads).