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.
Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain.
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%.
Recent developments in power system and internet technology introduced blockchain technology to energy trading. In addition, the growing penetration of distributed energy resources made the division of distribution systems into microgrids a tempting solution for technical and economical problems. In this paper, the feasibility of applying blockchain technology as an accounting system for energy trading within each island and between interconnected islands is considered. The distribution system is considered after being optimally divided into islands. Energy hubs, aggregated thermostatically controlled loads and aggregated electric vehicles (EVs) are considered. Optimal demand-side management is applied taking into consideration the uncertainties arising from renewable energy sources due to their nature, load forecasting, energy price due to bidding actions accompanying energy transactions, and energy exchange in EV parking stations due to EV availability patterns. The blockchain-based energy trading (BET) system is considered in three steps. The first step is carried out considering EV parking stations for energy trading between EVs and then between parking stations and island consumers with billing transfer between different blockchains. The second step considers the trading between prosumers and consumers within each island. In the third step, the BET system is applied for energy trading between interconnected islands. The feasibility of the proposed energy trading system is validated using a realistic case of the distribution system of Alexandria, Egypt. The simulation results show the positive impact of applying blockchain-based energy trading on energy cost.
This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers.
Decomposing the large distribution grids into interconnected microgrids (MGs) can potentially enhance the power system's efficiency, sustainability, resiliency, and reliability. However, energy management within the entire network would be more complicated and challenging. This article develops a novel energy management framework for interconnected MGs based on a blockchain technology. Utilizing the blockchain technology can potentially enhance the system security, and also reduce the system risks, mitigate financial fraud, and cut down the operational cost. A priority list is first defined to get into an efficient energy tradeoff within the interconnected MGs. Moreover, the incentive contract is proposed to provide a price discount for a party that purchases more power from one sub-MG. A stochastic framework based on the unscented transform technique is also established to manage the uncertainties associated with hourly load demands and output power of renewable energy sources. The proposed model is formulated as a mixed-integer linear programming problem and solved through the blockchain-based energy/power management algorithm. The case study includes residential, industrial, and commercial MGs-namely, three residential, one commercial, and one critical load (hospital). The simulation results show the high efficiency and effectiveness of the proposed model and validate its economic and reliability merits.
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.
Mohamed Hamouda, Mohammed E. Nassar, M.M.A. Salama
Microgrid planning philosophies are changing from islanding in the case of abnormal conditions to independent sustainability for constant secure, reliable operation. Large independent operators foresee a shift from gigantic-grid bulk generation and transmission to distributed generation (DG) from smaller, interlinked grid clusters. Customers and the community will benefit, but system operators and utilities will face challenges. Introduced to enhance electricity exchange and the energy market structure in such grids, transactive energy networks favor customers and DG owners, establish a utility business model, and enable power system innovation. Planners are also increasingly interested in blockchains for secure transactions. Blockchain-based transactive energy markets promise flexibility, transparency, security, competition, and superlative low-cost reliability, offering ideal energy-trading solutions in isolated microgrids and distribution-level markets. This article presents the development and case-study validation of a comprehensive transactive energy market framework with linked blockchain and power system layers, a novel market structure based on an end-user marginal price, and an adapted blockchain that fits intrinsic power system requirements. A new slot-ahead electricity market model is established through integration with the modified blockchain. With blockchain operation, monetary funds are managed equitably so that wallet billing rates for customers, utilities, and DG owners match broadcast smart-meter data.
The increasing penetration of renewable energy and its inherent uncertainty necessitate the development of energy storage in the power system. Currently, the value of energy storage is still not fully unlocked because of 1) misallocation between the energy storage demands and resources, 2) lack of an energy storage sharing mechanism. To solve the above limitations, this paper designs an energy storage sharing mechanism via blockchain. A bidding model is established to optimize the bidding strategies of energy storage in joint energy, frequency, and FRP (flexible ramping product) market. Then, a blockchain-based P2P (peer-to-peer) energy storage sharing mechanism in the joint markets is proposed to enable trustworthy and transparent trading. Simulation results on a Substrate private blockchain verify the efficiency of the proposed mechanism.
Renewable energy resources are key components of the sustainable social development that has been rapidly deployed in recent years. The proliferation of renewable energy resources promotes socioeconomic development in various parts of the world, and islandable microgrids (MGs) play an increasingly important role in such development. MGs represent a viable alternative to conventional bulk power transmission for addressing the vulnerabilities of long-distance power delivery from centralized generation units to distributed customer sites. A controllable MG equipped with on-site distributed energy resources (DERs), which could include distributed generators, energy storage, and economic demand responses, cultivates local resources to enhance the reliability, resilience, sustainability, security, and economics of local power systems.
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.
Godwin C. Okwuibe, Michel Zadé, Peter Tzscheutschler, Thomas Hamacher · 5 authors
The framework presented provides an open-source, blockchain-based, peer-to-peer energy market platform which can be used for testing different setups or for creating a microgrid peer-to-peer trading platform. The framework offers the following possibilities: · variations of the trading horizon, metering intervals; · simulations within a fraction of the real time; · variations of the number of participants; · multiple operated microgrids within one smart contract; · clearing mechanisms with discriminative prices or a market clearing price; · functionality to log data exchanged with the blockchain; production and consumption data of each participant, electricity exchanged within the microgrid and the main grid, token balances of all participants, · variation of the price ranges.
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.
Nov 1, 2020·2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
Peer-to-peer energy trading among microgrids has many advantages, e.g., increasing the utilization of renewable energies, reducing the dependence on the main grid and reducing energy cost. In this paper, we investigate a peer-to-peer energy trading problem among microgrids under uncertainties. To be specific, each microgrid intends to maximize its own utility in a local peer-to-peer energy market. Due to the existence of uncertainties in renewable energy output and power demand of all microgrids, and temporally-coupled constraints related to energy storage devices, it is very challenging to develop an optimal energy trading policy for each microgrid. To achieve the above aim, we propose a multiagent deep deterministic policy gradient (MADDPG)-based energy trading algorithm, which can help to find the optimal policy for each microgrid without requiring the generation and load information of other microgrids. Moreover, blockchain is adopted to guarantee the integrity of energy transaction data. Simulation results show the effectiveness of the proposed algorithm in the aspect of reducing energy cost and ensuring the security of transaction data.
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