https://www.jemat.org/ Journal of Energy Management and Technology en daily 1 Thu, 01 Jan 2026 00:00:00 +0330 Thu, 01 Jan 2026 00:00:00 +0330 https://www.jemat.org/article_235272.html This paper presents a deep learning-based adaptive robust control strategy for smart microgrids, aiming to simultaneously improve power quality, reduce active power losses, and enhance resistance against cyber-attacks. The proposed controller integrates error-estimation-based robust control with an adaptive deep neural network that dynamically updates control coefficients in response to uncertain operating conditions. In addition, an embedded attack detection and mitigation mechanism safeguards the system against threats such as false data injection, denial-of-service, and replay attacks. The effectiveness of the proposed approach is evaluated on a three-phase multi-bus microgrid under diverse load variations, disturbances, and cyberattack scenarios. Comparative results against classical PI, sliding mode control, H∞, and model predictive control schemes demonstrate that the proposed controller achieves lower total harmonic distortion, faster settling times, reduced active power losses, and higher reliability indices. These findings confirm the potential of the proposed method as a practical and efficient solution for securing and optimizing next-generation smart microgrids. https://www.jemat.org/article_229293.html Cryptocurrency mining, propelled by its high profitability and the availability of low-cost energy in certain regions, presents significant challenges to distribution networks, including unauthorized energy consumption, increased network loading, power theft, harmonic distortions, and degraded power quality. This paper introduces an innovative approach based on Harmonic State Estimation (HSE), which leverages the unique harmonic signatures of mining loads to accurately detect unauthorized cryptocurrency mining operations within distribution networks. The developed methodology could successfully identify the miners even in the presence of other harmonic-distorted loads such as steel industries, smelting plants, and textile factories. By analyzing harmonic measurements, harmonic power output, and power factor across various distinct scenarios involving varied harmonic loads alongside unauthorized mining activities, the proposed method precisely locates these operations. Implemented and validated using DigSILENT PowerFactory on standard test networks, the results underscore the method’s accuracy in differentiating mining activities from other harmonic sources, offering distribution network operators a robust tool to mitigate power theft, enhance network stability, and improve overall reliability. https://www.jemat.org/article_222603.html Optimal energy management in microgrids will increase their economic and technical efficiency, and it is usually carried out as an optimization problem for day-ahead operation strategies. Although many perspectives have been proposed for optimal operation, with increasing energy demand and imbalance between production and consumption, the lack of coherent planning to reduce costs and environmental consequences is still felt. Therefore, creating a new framework to consider uncertainties and operation issues simultaneously is necessary to increase reliability. For this purpose, in this paper, a two-level energy management system is presented at the first level determines the role of the load response program, the unit Synchronization, the generator unit production rate, and the storage charge and discharge rate, then the network reconfiguration in the presence of renewable energy sources is considered with the aim of maximizing the network operating profit and minimizing environmental pollutants at the second level. Also, the output power of renewable energy sources, including solar and wind, with uncertainty and scenario generation, has been considered. The presented model has been tested as a combination of two software, MATLAB and DigiSilent, on a 33-bus IEEE network to make it more realistic and increase the accuracy of the simulation. The results show that the load response program, along with the network feeder rearrangement and the problem of bringing the units into orbit simultaneously, in addition to reducing losses by 20% and increasing reliability by 10%, results in profitability for the microgrid operator. https://www.jemat.org/article_234580.html Energy hubs provide a unified platform for managing electricity, gas, and thermal energy; however, their responsiveness to uncertain operational conditions remains insufficiently addressed. To fill this gap, a single-building energy hub is modeled using real-world data from diverse Iranian climates in this paper. This study models a single-building energy hub using real-world data from diverse Iranian climates and introduces a sensitivity-driven optimization framework for official buildings. The configuration includes grid electricity, wind turbines, photovoltaic panels, geothermal pumps, solar water heaters, diesel generators, and natural gas. A mixed-integer programming approach minimizes operating costs under technical and policy constraints. Sensitivity analysis reveals that integrating renewable energy sources reduces operational costs by up to 27%, while CHP deployment alone achieves a 19% cost reduction under gas price volatility. Grid outages increase costs by 35% and lead to up to 22% load curtailment. These findings offer a scalable framework for designing resilient energy systems tailored to administrative infrastructure under uncertainty. https://www.jemat.org/article_242336.html The increasing adoption of electric vehicles has introduced a new electric load on the grid, which differs from the traditional load profiles found in networks. Additionally, renewable energy sources significantly influence the generation patterns in these networks due to their inherent uncertainty and large fluctuations in generation. This paper introduces a multi-objective distribution system expansion model that takes into account investments in new equipment, including renewable energy sources, energy storage, and electric vehicle charging stations. The required charging for electric vehicles is determined based on their travel patterns throughout the day, considering each charge. Uncertainty in both the load and renewable resources is addressed by generating possible scenarios derived from their probability distribution curves. The final stochastic model aims to minimize the expected expansion costs of the system, including the present value of investments, maintenance, generation costs, losses, and unmet demand. The deterministic equivalent model, which incorporates a random-axis scenario model, is an integer optimization problem solved using a genetic algorithm. Numerical simulations were conducted on a 9-bus network as a small example, followed by analysis on the standard 30-bus network. The results indicate that the implementation of distributed generation sources improves losses and voltage conditions, often leading to a preference for developing distributed generation rather than expanding substations. The findings suggest that the growth of distributed generation has resulted in significant delays in substation expansion, offering an alternative that can achieve much better technical outcomes in place of substation expansion. https://www.jemat.org/article_242951.html The growth of distributed generation resources, local electricity markets, and peer-to-peer energy exchanges has introduced challenges, notably balancing network technical constraints with maximum utilization of distributed generation units. Static operation envelopes (SOEs) and Dynamic operation envelopes (DOEs) have emerged as solutions, statically and dynamically setting network constraints over time, respectively. However, these envelopes often calculated by distribution system operators (DSOs) and imposed on users, may overlook participants' preferences, reducing profits and incentives to engage in local markets. Addressing this, the paper proposes a framework for determining feasible transaction regions (FTRs), established through agreements between energy communities (ECs) and DSOs. These FTRs represent optimal operating envelopes that consider uncertainties in ECs and DSO constraints. The framework models EC uncertainties using the chance constraint method and solves the optimization problem with the alternating direction method of multipliers (ADMM) on a standard IEEE 69-bus network using GAMS software. Results highlight the method's efficiency and accuracy compared to DOE and SOE approaches, effectively defining safe regions for peer-to-peer energy trading in distribution networks. This novel approach optimizes energy exchanges while respecting technical constraints and participant preferences. Also, this paper, by providing different confidence levels during simulations, verifies the proper efficiency of the proposed method in operating the uncertainty of distributed generations. The simulation results show cost reduction of ECs and loss reduction in the distribution network along with increment in active and reactive power exchanges compared to DOE and SOE methods. https://www.jemat.org/article_242952.html The increasing penetration of renewable energy resources and the digitalization of modern power systems have intensified the need for flexible, transparent, and market-oriented demand response mechanisms. However, existing DR frameworks remain largely centralized and lack robust infrastructures for secure, traceable, and tradeable flexibility exchange among distributed energy participants. In this paper, a blockchain-based peer-to-peer platform is proposed to enable the digital tokenization of DR capacity rights. Within the proposed framework, DR capacity is represented as a fungible token, allowing flexibility assets to be fractionalized, transferred, and exchanged within decentralized energy markets. To operationalize this concept, a four-stage token lifecycle model—comprising creation, bidding, transfer, and redemption— is developed and encoded through smart contract logic, facilitating automated transaction management and lifecycle traceability. The proposed Digital Tokenized Demand Response Exchange establishes a decentralized architectural layer designed to enhance transparency, auditability, and procedural automation in flexibility trading environments. By transforming DR capacity into a standardized digital asset, the framework supports broader prosumers participation and contributes a conceptual foundation for the evolution of decentralized flexibility markets within future digital energy ecosystems. https://www.jemat.org/article_244398.html Net Zero Energy Buildings (NZEBs) and nearly Zero Energy Buildings (nZEBs) have evolved from individual building concepts toward broader applications at the urban neighborhood scale. This paper presents a bibliometric analysis of energy management practices in zero energy urban neighborhoods, focusing on emerging research trends over the period 2020–2025. Using VOS viewer software, we analyze 584 peer reviewed articles retrieved from major scientific databases. The study maps the co occurrence of keywords, country level research activity, and author collaboration networks. Results indicate exponential growth in publications from 2023 onward, driven by the convergence of zero energy concepts with artificial intelligence, machine learning, deep learning, and digital twins. Key thematic clusters include energy management systems, HVAC optimization, renewable energy integration, smart grids, and the Internet of Things. Geographically, research leadership has shifted from Europe and the United States (2021–2022) to Asian countries (China, India, South Korea, Japan, Iran) by 2023–2025. A persistent research gap is identified: zero energy neighborhood development in hot and humid climates, particularly in developing economies, remains severely underrepresented due to high upfront costs and long payback periods. The review synthesizes design hierarchies passive strategies first, followed by efficient active systems, then on site renewable generation and discusses the limitations of current AI applications, including data availability, generalizability, interpretability, and the absence of standardized benchmarks. https://www.jemat.org/article_244594.html In modern smart grids, prosumers and distributed energy resources increasingly participate in local electricity markets, while higher renewable penetration raises the need for reserve capacity and operational flexibility. This paper proposes a joint peer-to-peer (P2P) market framework for energy and reserve trading with product differentiation, in which agents negotiate bilaterally with neighboring peers and can trade both energy and reserve capacity. The resulting social-cost minimization problem is formulated and solved using a centralized benchmark together with ADMM-based decentralized and distributed clearing mechanisms. In the fully decentralized market, a coordinator-free consensus ADMM is adopted for direct P2P trading, whereas the hybrid framework employs an exchange ADMM to clear intra-community trades prior to decentralized negotiations across communities. Numerical studies quantify the trade-off between centralized and decentralized clearing in terms of objective value, computation time, and communication burden. The results further demonstrate how product-differentiation coefficients influence market prices and traded quantities. Finally, two coefficient-setting strategies are introduced to steer decentralized market outcomes toward centralized-like or hybrid-like operating points, thereby enabling the market operator or platform designer to influence trading behavior under decentralized clearing.