Gary Hahn, Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth
Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.
Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink, Aaron Werth
In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs.
Musse Mohamud Ahmed, Mohammad Kamrul Hasan, Muhammad Shafiq, Md Ohirul Qays · 7 authors
Utilities produce and supply products following local requirements and with the synchronizations which connect subscribers. Harmonics is a power efficiency/quality variable caused by electronic devices that domestic and industrial consumers use. The famous IEEE Standard 519 is maintained to calculate harmonic limits, which ensures power efficiency. In a standard power system, currents and voltages generate pure sine wave signals during regular operations. As harmonics influence the power system, they cause interference in the sine wave signals. So, the best practice method should be used to resolve the harmonics issue. One of the problem-solving techniques of harmonics is the measurement and reduction of harmonics detection, and it uses Fast Fourier Transform (FFT). Therefore, power output should assess in a peer-to-peer Blockchain scheme by measuring and minimizing harmonics detection. This paper uses a Shunt Active Power Filter (SHAPF). It describes the simulation analysis and reduction of harmonics detection in a peer-to-peer interconnected 3-phase power system with the help of an FFT algorithm. This research was carried out to assess the efficiency of the AC signal by collecting, processing, and evaluating power data. Using the shunt active filter, the proposed design outperformed the traditional methods for both six and twelve pulse rectifiers, achieving total harmonic distortion (THD) of only 1.42% and 0.92%, respectively.
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
Lee Thomas, Chao Long, Pete Burnap, Jianzhong Wu · 5 authors
An electricity supply smart contract was developed and demonstrated to perform pre-time-of-use price negotiation between demand and generation and post-time-of-use settlement and payment. The smart contract was demonstrated with 1000 loads/generators with usages simulated using lognormal probability distributions. It combines payment of deposit, negotiation of price based on estimates, settlement based on actual usage and enactment of payments using crypto-currency. The settlement procedure rewards customers that adjusted to balance the system. The smart contract was written in the solidity programming language and implemented with a simulated Ethereum blockchain using testrpc and go-ethereum. In the example test case, a price was agreed, settled and payment enacted.