To accelerate the large-scale integration of renewables for transition to a low-carbon energy system, the deployment of megawatt-scale electrolyzers and fuel cells systems is important. For their widespread integration and effective operations, the appropriate consideration of electrolyzer and fuel cell degradation is necessary. To this effect, this study presents two novel comprehensive degradation cost models, which are developed based on their dynamic degradation rates and life-cycle operating hours. The proposed degradation cost models are state transition-based method and ramp rate-based method, designed to quantify degradation as a function of input/output current density and ramp rates, respectively. To ensure their practical application in megawatt-scale power system operations, wherein their dispatch is typically based on electric power rather than electrochemical current density, these models were reformulated in terms of the power consumption/production. To estimate the parameters of the proposed degradation cost models, three regression approaches — linear, quadratic and third-degree polynomial — were carried out. Comparative analysis demonstrates that higher-degree polynomial models offer improved accuracy by capturing the nonlinear degradation behavior during dynamic operations, with state transition-based models consistently showing superior performance. Furthermore, the prediction performance of the estimation models are compared with the actual costs, followed by a sensitivity analysis of the estimated cost parameters.

Distribution system operators (DSOs) are entrusted with the responsibility of activating distribution services from distributed energy resources (DERs) and enabling DER participation in Independent System Operator (ISO)-managed electricity markets. This paper presents a method for DSOs to construct service-aware, distribution-feasible bid-offer curves for use in DSO-to-ISO market interfacing by embedding distribution-service actions in a single-interval Optimal Power Flow (OPF) sweep at the transmission-distribution (T-D) interface, with DERs modeled as active power sources. At each sweep setpoint, the OPF co-optimizes solar photovoltaic (PV) curtailment and battery energy storage system (BESS) operation. The marginal price and active power at the T-D interface form the bid-offer curve. The 33-bus distribution test system is used to develop the bid-offer curve, which exhibits breakpoints that reflect voltage, thermal and DER limits across a feasible range of about 2 MW export to 1.6 MW import. The method unifies distribution-service activation, renewable curtailment, and DSO-to-ISO market interfacing within a single optimization framework.

Green Hydrogen Systems (GHS), wherein the hydrogen produced via electrolysis using surplus renewable electricity, is a zero-carbon solution to meet the de-carbonization goals. The performance of a GHS is affected by their degradation characteristics. This paper presents a GHS-integrated Uniform Marginal Price (UMP)-based mathematical model for wholesale Day-Ahead Market (DAM) settlement. The model accounts for detailed physical and operational characteristics of the GHS by including its linear degradation cost function to account for cyclic aging behavior of the electrolyzers and fuel cells, in accordance with FERC Order 841. The proposed DAM clearing model is tested on the IEEE 24-bus Reliability Test System (RTS) incorporating wind and solar PV facilities. The impact of inclusion of degradation cost on the market clearing price, GHS unit operations and system emissions is analyzed across four distinct scenarios. Results demonstrate that incorporating degradation costs in the mathematical model leads to a more rationed scheduling of the electrolyzer and fuel cell units, avoiding their over-utilization that can accelerate component degradation.

This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors. The proposed methods are tailored for systems with a limited number of spatially distributed measuring devices, which measure voltage magnitudes at specific nodes and some line current magnitudes. The overall approach employs a problem decomposition strategy to divide the problem into smaller subproblems, which are addressed independently. The techniques for identifying switch states and system phases are based on heuristics and a binary optimization problem using correlation analysis of the measured time series. The estimation of equipment parameters is achieved through a data-driven regression approach and by an optimization problem, and the identification of cable types is solved using a Mixed-Integer Quadratic Programming solver. To validate the presented methods, a realistic grid is used and the presented techniques are evaluated for their resilience to data quality and time resolution, discussing the limitations of the proposed methods.

1.Introduction Engineers,policymakers,and governments are currently facing the pressing global challenges of climate change and the energy crisis.To address the continuously increasing demand for energy and mitigate environmental damage,energy conservation and emissions reduction have become strategic priorities for sustain-able development[1].Nations worldwide have reached a consen-sus on reducing carbon emissions and have introduced various policies and actions,such as the carbon peak and carbon neutrality targets proposed by China[2,3].Globally,fossil fuels(i.e.,coal and oil)make up over 80％of primary energy consumption,placing sig-nificant pressure on carbon-reduction efforts[4].Consequently,developing future power systems centered on low-carbon,clean,and renewable energy is crucial for achieving carbon-reduction goals.

This paper presents a novel framework and a comprehensive mathematical model of an Augmented Capacity Market, which includes the capacity participation from demandresponse (DR), import and transmission capacity enhancement (TCE) providers, in addition to generators. The auction model for the augmented capacity market considers the detailed physical, capacity arbitrage and operational characteristics of participants. The proposed model is tested considering capacity offers scaled equivalent to a mid-sized ISO.

Methods for distribution system state estimation in Low Voltage (LV) distribution grids are discussed in this paper, for systems with a high penetration of Distributed Energy Resources (DERs) such as solar generators and heatpumps. The proposed methods are specifically designed for LV grids with sparse measurement availability, such as feeders with measurements only at the distribution transformer, as is typically the case in some European LV grids. For these cases, device locations, temporal data, and weather data are used in the proposed techniques to estimate variables at unmeasured grid nodes. The impact of smart meters is also investigated by simulating the impact of individual smart meter measurements on the estimation results. The proposed methods are based on time series disaggregation of transformer measurements, such as thermoelectrical demand, baseload, and solar generation, enabling improvements over existing Pseudo-Measurement (PM) generation techniques. Furthermore, the paper presents approaches for estimating voltages and currents in the feeder using both actual and PMs, based on classical estimation methods and interval estimation techniques for unmeasured variables. The results for an realistic German LV grid show that the proposed disaggregation step allows to significantly improve the results of the state estimation results over state-of-the-art methods.

This paper proposes an adaptive Alternating Direction Method of Multipliers (ADMM) framework for distributed energy management in Peer-to-Peer (P2P) energy trading within Active Distribution Networks (ADNs). The model enables decentralized coordination of houses equipped with Photovoltaic (PV) generation and Battery Energy Storage System (BESS), while preserving user privacy. Centralized optimization, Dual Decomposition (DD), and ADMM-based approaches are compared against a baseline scenario of individual household optimization without P2P trading. A heuristic scaling-based adaptive ADMM strategy was proposed and compared with the residual balancing strategy, which reduced the convergence iterations compared to the standard ADMM and DD, while achieving comparable solutions. A comprehensive analysis involving four houses with varying energy resources demonstrates the significant benefits of P2P trading, including a reduction in energy costs and a significant decrease in grid exchanges. Both Time-Of-Use (TOU) pricing and Feed-In-Tariffs (FITs) are considered, with results showing that higher FITs lead to increased P2P energy transactions.

Control methods for Home Energy Management Systems implemented with traditional optimization techniques and state-of-the-art Machine Learning methods are presented and compared in this paper in the context of their impact on and interactions with Active Distribution Networks. Thus, model-based methods based on Model Predictive Control algorithms with different prediction qualities are first described and compared against model-free methods based on imitation learning and reinforcement learning. A practical, state-of-the-art, heuristic, rule-based controller is used as the baseline. An in-depth comparison is performed using metrics consisting of objective function values, grid constraint violations, and computational time. The results of applying these Home Energy Management Systems to a realistic German low voltage benchmark grid with 13 connected households, each containing solar generation, a battery storage system, and electrical loads are discussed. It is demonstrated that model-based and model-free methods can achieve improvements over typical rule-based methods, with varying performance in terms of objective function values and grid constraint violations depending on the forecasts, at the cost of higher computational complexity. Furthermore, model-free methods are shown to have in general low computational burden at higher objective function values with more grid constraint violations, with imitation-learning-based techniques proving to be the best compromise for practical applications.

The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand-supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency.

The transition to a low-carbon energy system requires large-scale renewable integration into electricity grids. However, the variability and uncertainty of these resources pose challenges to grid reliability and stability. Energy storage technologies help mitigate these issues, with hydrogen storage emerging as a promising option for both short- and long-term energy storage. Green hydrogen, produced via electrolysis using surplus renewable electricity, functions as both an energy carrier and storage medium, that can possibly reach 49 Mtpa by 2030 [1]. When combined with hydrogen tanks and fuel cells, Green Hydrogen Systems (GHS) can flexibly interact with the grid by utilizing energy arbitrage and can provide ancillary services.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

With increasing penetration of Electric Vehicles (EVs) into the transportation system and smart electricity grid, there is a growing need for integrating them into Home Energy Management Systems (HEMS). This integration within HEMS introduces dynamic user behaviors and time-varying charging demand, thus posing challenges for the HEMS. To mitigate these challenges, this paper proposes a charging model for heterogeneous EVs that covers the range of Plug-in Hybrid EVs (PHEVs), Range-Extender EVs (REEVs) and Battery EVs (BEVs) with/without heat pumps. The proposed heterogeneous EV charging model considers weather conditions, estimated mileage and driver’s experience to describe the dynamic charging demand and the anxiety level influencing their behavior. To optimize the HEMS operation, minimizing the energy cost and ensuring comfort, this paper introduces an offline Deep Reinforcement Learning (DRL) algorithm which learns directly from pre-collected datasets, avoiding the cost and safety issues associated with continuous real-world interactions. The algorithm incorporates the Huber loss and a Q-quantile estimator to mitigate performance degradation from dataset anomalies such as data noise, sensor failure and human error, resulting in more robust HEMS optimization strategies. Experimental results demonstrate the method’s effectiveness in reducing total costs and analyze the performance of household devices with two different electricity rates.

Encoding rule-based curtailment orders for Distributed Energy Resources (DERs), such as last-in-first-out (LIFO), into distribution optimal power flow (DOPF), can cause curtailment inversion, where DERs intended to be curtailed later are curtailed ahead of those intended to be curtailed earlier. This paper proposes a pseudo-cost encoding method that explicitly manages curtailment inversion, introducing a controlled “softness” in enforcing the order through tunable cost gaps. Wide (large step size) and narrow (small step size) cost-encoding strategies are examined to show how varying cost gaps affect inversions. The proposed method allows regulators and utilities to transparently balance strict curtailment order with networkdriven effects.

This paper presents a novel framework with new mathematical models that integrate Demand Response (DR) and Battery Energy Storage Systems (BESSs) simultaneously in a Locational Marginal Price (LMP)-based Multi-Settlement Market (MSM), i.e. a coordinated Day-Ahead Market (DAM) and Real-Time Market (RTM). A new set of generator ramping constraints, developed from the DAM settlement, and referred to as Day-Ahead Load-Following (DALF) Ramp, are included in the RTM auction model. The performance of the mathematical models are tested on the IEEE 24-bus Reliability Test System (RTS) by carrying out various case studies, scenarios, uncertainty and sensitivity analyses. Effect of DR and BESS characteristics such as level of participation, initial state-of-charge (SOC), discharge rate, etc. on market settlement is examined. The results demonstrate the merits of the proposed framework, and the impact of the DALF Ramp, DR and BESS inclusion in the MSM auction models on marginal prices, market settlement and system operation. It is noted that the system with DR and BESS in the MSM hedges real-time prices and effectively supports system operation during uncertain events such as line and generators outages, changes in demand or in generation from renewables.

Globally, efforts are being made to fight against the climate change issue and different mechanisms are being explored to achieve a net-zero emission (NZE) system. Among different energy sectors, the electric power sector is experimented with the most, worldwide, to be an NZE sector, with the thrust from electrification and energy transition drive. Hydrogen is anticipated to be one of the promising alternatives in accelerating this ambitious goal of NZE. To this effect, this paper examines the impact of the inclusion of multi-colored hydrogen systems (MCHSs) in a uniform marginal price (UMP)-based day-ahead electricity market (DAM) on overall system emissions and market clearing price (MCP). A detailed mathematical model is formulated as a mixed integer programming (MIP) problem considering the physical and operational characteristics of market entities and is tested on the IEEE 24-bus Reliability Test System (RTS) with renewables. Results demonstrate the comparative analysis of different colors of hydrogen systems inclusion on emissions and MCP profiles over a 24-hour horizon.

Benefits accrued by virtue of the presence of microgrids have led to their increased deployment beyond their original objective of supplying power to the remote communities. However, in order to achieve a zero emission energy sector, the challenge is to design a carbon-neutral microgrid. This paper presents a novel, optimal design for a decarbonized microgrid taking into consideration the concept of sector-coupling, by integrating the electric, heat/thermal, hydrogen and transport sectors. The microgrid also includes wind facilities, solar PV panels, green hydrogen system (fuel cells, electrolyzers, storage tanks), Fuel Cell Electric Vehicles (FCEVs) and Battery Energy Storage Systems (BESSs). The real isolated microgrid of Kasabonika Lake First Nation (KLFN) in northern Ontario, Canada, is considered for the design studies and to evaluate the techno-economic feasibility. The effect of (US) Inflation Reduction Act of 2022 (IRA2022) is examined. Results demonstrate the practicability and techno-economic merits of the proposed Decarbonized Sector-coupled Microgrid (DCSCMG). The proposed DCSCMG is compared to the existing diesel-based KLFN microgrid on economic metrics, levelized Cost of Energy (COE) and emissions. Further, the advantages offered by inclusion of BESSs and/or sector-coupling are investigated in the context of net-zero.

The planning and operation of power systems necessitates accurate modeling of generation, demand and energy storage. This includes taking into account the stochastic nature of renewable resources, electricity prices and load profiles. This study presents an innovative approach to develop cumulative distribution functions (CDFs) for Li-ion battery dispatch during its charging and discharging states. The CDFs are obtained by executing an hourly dispatch model over a one-year time-frame utilizing actual zonal electricity price data of New York City (NYC) to determine the optimal hour-by-hour dispatches of the battery. This dispatch data are clustered by four distinct seasons and two different day-types (weekdays, weekends) to replicate the energy price profile. The battery charging, discharging and inactive states are derived from the clustered profiles, along with their respective time-frames. Thus, the CDFs are formulated for each season, day type and battery condition (charging or discharging) according to a probabilistic analysis conducted on an hourly basis.

Energy transition, shifting from fossil-fuel based to clean resources, is a critical step toward achieving net-zero emission targets, and is being explored worldwide. Green hydrogen is a potential zero-carbon solution to meet decarbonization goals. In this context, this paper presents a novel framework and mathematical model to integrate hydrogen-based emission free resources (HEFRs) in a locational marginal price (LMP)-based day-ahead market (DAM). The model takes into account the detailed physical, energy arbitrage and operational characteristics of the HEFRs. The proposed model is tested on the IEEE 24-bus Reliability Test System (RTS) with PV and wind included, and is formulated as a mixed integer programming (MIP) problem. The results demonstrate the benefits of the proposed framework and the impact of HEFRs participation on market settlement, marginal prices, system emissions and system operation during normal, uncertainties and congestion scenarios.

The adoption of Electric Vehicles (EVs) and solar Photovoltaic (PV) generation by households is rapidly and significantly increasing. Utilities are facing the challenge of efficiently managing EV and PV resources to help mitigate the undesirable effects on grid operation. Existing approaches to solve these issues depend on accurate but hard to predict behavior of EVs and PVs, detailed knowledge of customers, and grid infrastructure, all of which complicate the effective deployment of these resources. Motivated by these practical challenges and in collaboration with industry partners working on addressing these issues, this paper proposes a two-level data-driven smart controller for EV charging in distribution systems. The controller is modeled as a Deep Reinforcement Learning (DRL) agent, which coordinates the charging rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) services; at the Low Voltage (LV) level it aims to maximize the EVs' state of charge at departure while avoiding the overloading of the MV/LV distribution transformers. The controller is verified through simulations on an actual utility grid facing the aforementioned challenges, demonstrating the effectiveness and practicality of the proposed DRL-based smart charging approach.