This paper introduces a centralized peer-to-peer (P2P) market framework for coordinated active and reactive energy trading in active distribution systems with high penetration of distributed renewable energy sources. The proposed framework adopts a three-stage process that enables allocation of active energy and subsequent allocation of reactive energy, and real-time settlement. A sequential optimization approach is employed to determine the scheduled allocation of active and reactive energy by maximizing the conflicting multi-objective welfare functions of sellers and buyers. The approach focuses on monetizing P2P reactive energy trading to enhance peer welfare, which incentivizes prosumers supporting the grid with reactive power while others remain limited to active energy trading. Real-time deviations are managed via grid interaction and post-metering penalties to enforce adherence. Validation on a CIGRE LV benchmark network confirms that the proposed framework outperforms the net feed-in tariffs of Kerala and Tamil Nadu, India, by yielding higher peer earnings and fostering renewable energy integration. The percentage difference in hourly earnings of peers, considering the scheduled energy transaction in the P2P framework with reference to the net feed-in system for sellers, is between 105% and 160%, respectively.
Decarbonization requires integrating new technologies and advanced systems to harness energy sources, such as those found in geothermal reservoirs. Due to drilling costs, the reuse of hydrocarbon wells is being promoted. The objective was to numerically model the interaction between hydrocarbon wells with high geothermal potential. Hydrocarbon wells and geothermal zones in Mexico were overlaid, and prospects were identified based on topography, geothermal, geological, and inactivity criteria. A stratigraphic profile was constructed within a control volume (4,244 m × 5,655 m × 4,500 m) containing 84,150 nodes and 70 wells. Only well pairs (X-1, X-2) and (X-4, X-3)—separated by 360 m and 430 m, respectively—exhibited relevant thermal characteristics. In Fortran90 simulations, average temperatures were 160°C and 150°C, with a maximum of 166°C, at depths of 4500 m, 4000 m, and 4500 m, respectively. A 10% variation in thermal conductivity affected the initial temperature by ±3%, a figure lower than the expected ±20°C. Isotherms plotted at 25°C intervals revealed thermal non-linearity. The Recklaw and Weches formations were better thermal conductors, while the Queen City formation retained heat more effectively. Geothermal energy can be harnessed through direct-use applications, even for neighboring wells lacking specific data. Conversion to petrothermal systems requires analysis of geomechanics, hydraulic fracturing, and mechanical integrity. This research presents the first numerical model in Mexico for the assessment of hydrocarbon wells for geothermal potential—a sustainable energy transition alternative aimed at decarbonization and the decentralization of renewable energy, initially at a regional scale and subsequently at a national one.
As energy systems become increasingly interconnected and complex, robust modeling tools are needed to capture regional interactions and guide sustainable investment decisions. This study models multi-region, interconnected energy systems within the long-term expansion planning problem using the OSeMOSYS framework. While the standard OSeMOSYS formulation allows the representation of multiple regions, it does not explicitly represent inter-regional electricity exchanges. The proposed approach extends the original formulation by introducing explicit modeling of transmission links, including losses, investment costs, operational limits, and expansion requirements. The approach extends existing formulations to represent inter-regional electricity exchanges in greater detail, incorporating losses, costs, operational limits, and expansion requirements. A case study of the Brazilian energy system is presented, structured into multiple scenarios that vary in temporal, spatial, and technological resolution. Results show that resolution choices significantly influence model outcomes, including installed capacities, energy mix composition, and inter-regional transmission needs. Higher temporal resolution leads to divergent investment pathways, particularly in offshore wind and natural gas, while higher spatial resolution emphasizes regional resource allocation and transmission expansion. The analysis also reveals the computational trade-offs of increased model complexity, highlighting limitations of current solvers when addressing high-resolution, large-scale systems. Findings demonstrate the importance of defining clear modeling objectives and scope to balance detail, tractability, and policy relevance. The proposed methodology offers valuable guidance for energy system planners and researchers, enabling more transparent assessment of trade-offs in model construction and supporting informed decision-making for sustainable energy transitions.
This paper proposes a generalized framework for the integrated planning of Electric Vehicle Charging Stations (EVCS), Photovoltaic (PV) systems, and capacitors in distribution networks using a sequential optimization strategy aligned with practical planning dependencies. The three-stage approach addresses multi-stakeholder economics and reliability–cost trade-offs. To establish the planning baseline, Stage-1,determines EVCS locations that minimize total network losses using Particle Swarm Optimization (PSO). Stage-2 employs Multi-Objective PSO (MOPSO) to size PV systems by jointly optimizing conflicting utility and prosumer Net Present Values (NPVs), considering bill savings, export revenue, lifecycle costs, loss reduction, interruption savings, deferral benefits, FiT expenses, and PV degradation, with the optimal solution selected using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Stage-3 applies a similar framework to co-optimize capacitor number, size, and placement, balancing investment costs with reliability. Validation on the 33-bus system under probabilistic uncertainties in load, solar irradiance, and EV charging shows reduced losses, improved economic performance, and enhanced stability and reliability. Results demonstrate that the coordinated EV-PV-capacitor solution reduces active power losses by 59.0%, improves minimum voltage by 6.2%, and reduces SAIFI, SAIDI, and AENS by 17.9%, 20.4%, and 30.3%, respectively. Sensitivity analysis of FiT and electricity prices identifies economically favorable tariff combinations for scalable and cost-effective grid integration.
The integration of residential PV systems with electric vehicles (EVs) offers significant opportunities to enhance self-consumption, reduce reliance on the grid, and lower household energy costs. Effective PV system sizing, however, is challenged by uncertainties in PV generation, household electricity demand, and EV charging behavior. While traditional approaches often ignore uncertainties or assume predefined values for uncertainties, this paper develops an adaptive robust optimization framework for sizing residential PV systems in EV-integrated households, explicitly accounting for operational uncertainties and calibrating the uncertainty parameters using a Genetic Algorithm. The proposed model explicitly incorporates multi-source uncertainty using a budgeted adaptive robust optimization approach. Investment decisions, such as PV capacity, are modeled as here-and-now variables, while operational variability is captured as wait-and-see decisions responding to uncertainty realizations. To mitigate the inherent conservatism of robust optimization and identify optimal uncertainty settings, a Genetic Algorithm–based calibration is integrated. The GA iteratively searches for the best combination of uncertainty parameters through simulation-based evaluation, balancing robustness and economic efficiency. The results show that calibrated uncertainty parameters provide a cost-effective and resilient system design, avoiding oversized PV capacities while improving household energy performance by reducing the cost of energy up to 18%.
