Search Results (138)

With the ongoing advancement of China’s “dual carbon” strategy, wind power and photovoltaic installed capacity have grown rapidly, making renewable energy a critical pillar for building a new-type power system. However, their inherent intermittency, variability, and uncertainty pose significant challenges to the secure and stable operation of modern power systems. As renewable energy capacity continues to expand, regions rich in renewable resources increasingly coincide with areas characterized by relatively underdeveloped grid infrastructure. This has resulted in insufficient renewable energy accommodation on the generation side and inadequate transmission capacity within the grid. Consequently, the integration of renewable energy faces growing challenges, including spatial mismatches between generation and demand, insufficient system flexibility, and increasing pressure on grid security and reliability. This study presents a systematic review combined with a quantitative techno-economic assessment of electrochemical energy-storage deployment in renewable-rich and weak-grid regions. Three representative electrochemical energy storage technologies, namely lithium-ion batteries, sodium-ion batteries, and all-vanadium flow batteries, are quantitatively evaluated using technical indicators and levelized cost metrics, including the levelized cost of energy (LCOE) and levelized cost of storage (LCOS). The results show that lithium-ion batteries exhibit the best overall techno-economic performance, with LCOE/LCOS values of 670/440 CNY kWh⁻¹, compared with 660/840 CNY kWh⁻¹ for sodium-ion batteries and 690/490 CNY kWh⁻¹ for all-vanadium flow batteries. Additionally, a 10%/2 h energy storage system improves day-ahead power prediction accuracy, ultra-short-term prediction accuracy, and the correlation coefficient by 67%, 44%, and 18%, respectively. These findings establish a quantitative basis for linking energy storage technology selection with regional resource–grid conditions and operational requirements, thereby supporting coordinated source-grid-load-storage planning and cost-effective and reliable renewable energy integration in weak-grid regions. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may lead to infeasible schedules during deployment and weaken their reliability in digital-shadow-assisted operation. In addition, conventional cost-oriented scheduling objectives do not explicitly account for the carbon impact of real-time imbalances caused by forecast errors. To address these challenges, this paper proposes a digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems. The main methodological contribution is a feasibility-by-design neural decoder that embeds hard physical constraints directly into the network forward pass. By classifying devices into non-memory fast units, non-memory ramp-limited units, and memory-type storage devices, the decoder applies tailored transformations to enforce capacity limits, ramp-rate restrictions, state-of-charge dynamics, and terminal energy consistency by construction. Therefore, the generated schedules are physically feasible without relying on post-hoc repair. In parallel, a carbon-first objective is developed to minimize both scheduled emissions and imbalance-driven emissions, allowing the scheduler to reduce not only planned carbon output but also the carbon impact of real-time corrective actions. Forecast uncertainty is represented through a digital shadow that stores historical forecast-error patterns and generates augmented training scenarios. Case studies based on U.K. data show that the proposed framework produces fully feasible schedules and reduces annual CO₂ emissions by approximately 4.0% compared with a forecast-driven baseline, with larger benefits during high-demand periods. These results demonstrate that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty. Full article

In modern power systems, the rapid growth of renewable energy capacity, such as wind and solar photovoltaic (PV) power, has led to a decline in system equivalent inertia and primary frequency regulation margin. At the same time, net load fluctuations have intensified across multiple time scales, making it more likely for the RoCoF, frequency nadir, and quasi-steady-state frequency deviation to approach safety limits following disturbances. To achieve a balance between frequency security and economic operation, this paper proposes a two-stage stochastic frequency-security-constrained unit commitment (FSC-SUC) model tailored for scenarios with high renewable energy penetration. The day-ahead hourly dispatch stage jointly determines the on/off status and reference output of synchronous units and the reservation of slow frequency regulation capacity, as well as energy storage charging and discharging plans, SoC trajectories, and the reservation of fast frequency regulation capacity. The intraday minute-level real-time dispatch stage accommodates prediction errors through scenario-based rescheduling and ensures the deliverability of both slow and fast frequency regulation capabilities via commitment consistency constraints. To address the challenge of directly embedding frequency nadir constraints into mixed-integer optimization, this paper employs a modeling approach that combines analytical criteria with linear surrogate constraints. The RoCoF and quasi-steady-state frequency deviation are specified via aggregated analytical constraints, while the nadir is embedded into the main problem after generating samples offline using a simplified frequency response model and training a polyhedral linear surrogate for external approximation. The safety margin is then calibrated using high-quantile residuals from the validation set to ensure conservativeness. Case studies on the IEEE 33-bus system under different renewable penetration levels demonstrate that the proposed method significantly reduces the probability of frequency nadir violations and load-loss risk with only a modest cost increase while also improving coordination between fast and slow frequency regulation. Full article

To address renewable generation and load uncertainty in multi-campus integrated energy systems, this paper proposes a distributionally robust day-ahead–real-time coordinated scheduling model under a cloud-edge collaborative architecture. The studied system consists of photovoltaic, wind power, and combined heat and power campuses, each equipped with energy storage and transferable load resources. The cloud layer determines the day-ahead baseline dispatch plan, while the edge layer performs scenario-dependent real-time corrections. To improve adaptability to adverse operating conditions, bounded forecast-error scenarios are constructed, and a conditional value-at-risk-based distributionally robust objective is formulated. Meanwhile, a soft day-ahead–real-time energy-binding mechanism is introduced to maintain plan-execution consistency while allowing necessary real-time adjustments. Case studies show that, compared with the cases without peer-to-peer energy exchange, demand response, and energy storage, the proposed model reduces the objective value by 5.22%, 10.96%, and 5.05%, respectively. Sensitivity analysis and stress tests verify its feasibility and robustness under increased uncertainty and reduced flexible-resource capacities. Full article

Energy storage systems (ESSs) installed alongside traditional photovoltaic (PV) power plants are primarily used to track planned output, which often results in low utilization rates and extended payback periods. Moreover, existing research inadequately addresses actual grid frequency fluctuation characteristics and lacks multi-timescale optimization frameworks. To address these issues, this paper proposes a day-ahead and intraday multi-market coordinated rolling optimization strategy that integrates energy market trading with Automatic Generation Control (AGC) frequency regulation services through a zone-based frequency regulation control strategy. The strategy first defines distinct regulation zones based on regional control deviations, enabling a dynamic power allocation approach for the energy storage system. Recognizing that conventional constant power control can lead to battery overcharging, over-discharging, and reduced cycle life, the strategy introduces state of charge (SOC)-based variable power charging and discharging constraint coefficients. These constraints ensure the battery operates safely within its optimal range. Furthermore, an electrochemical energy storage life decay model is developed to quantify battery degradation. To accommodate the uncertainty in PV output, Latin hypercube sampling is employed. A day-ahead dispatch model is established to maximize the system’s total daily operating revenue, and rolling optimization is applied during the intraday phase to correct deviations from the day-ahead forecast. Finally, simulation studies using actual data from a PV power plant demonstrate that the proposed strategy achieves a total daily revenue of 107,477 ¥, representing a 24.6% improvement over energy market-only participation; battery aging costs are reduced by 11.1% compared to the scenario without zone-based frequency regulation control. Results indicate that the proposed strategy effectively balances battery life degradation against market revenue, significantly improving the overall operational efficiency and economic viability of PV-storage hybrid systems. Full article

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management. Full article

In electricity–hydrogen coupling systems (EHCSs), the uncertainty of renewable energy generation (REG) tends to impact electrolyzers (ELs) in the following ways: (1) input powers of ELs are prone to fluctuations; (2) ELs are forced to operate under variable load states. Consequently, both impacts will reduce the service life of ELs. In this paper, considering the start–stop characteristics and combined operation modes of multiple ELs, a two-stage multi-time-scale energy allocation strategy (MSEAS) is proposed to mitigate the impacts of REG uncertainty and optimize the energy allocation for EHCSs. First, five refined operating states of ELs, such as shutdown, cold standby, low-load, variable-load and overload, are formulated as mixed-integer constraints and embedded into the system-level energy optimization model. Second, to mitigate power fluctuations caused by REG, a day-ahead optimization is employed to plan the power allocations of ELs, lithium batteries, fuel cells, and the grid with a 1 h time step; and then an intra-day rolling optimization is employed to adjust the operating states and power outputs of the above units with a 4 h window and 15 min step. Third, by enabling multiple ELs to flexibly operate in a combined mode, power-sharing mode and switching mode, the proposed MSEAS can refine the operation powers of ELs and reduce their start-up frequency. Comparative case studies are conducted in the off-grid and grid-connected operation tests, and the relevant results verify that the proposed MSEAS can effectively prevent the frequent start–stop of ELs, which contributes to extending the service life of ELs and reducing the system operating cost. Full article

In this paper, we propose a self-learning digital twin (SLDT) architecture that incorporates real-time battery degradation modeling and optimum operational management for grid-scale lithium-ion battery energy storage systems (BESS). This work extends the Adaptive Real-Time Degradation Model (ARDM) framework to allow real-time updates of the parameters based only on live operational data without pre-cycling experiments and further improves its robustness under various depth-of-discharge (DoD), charging/discharging current (C-rate), and temperature conditions. The ARDM is incorporated in a real-time digital twin that maintains synchronized health, state of charge (SoC), and degradation cost predictions. The digital twin is linked to an Optimization and Control Layer (OCL), which plans the charge/discharge day-ahead in advance based on dynamic power rates. The Musical Chairs Algorithm (MCA) is used for parameter identification and scheduling due to its better convergence characteristics compared to swarm-reduction forms of benchmark optimization algorithms. Experimental validation is carried out on two commercial 48 V Li-ion modules with various cycling patterns, and sub-millipercent root-mean-square error (RMSE) is achieved in capacity-fade tracking. The economic analysis for a 5-MW/10-MWh system indicates that dynamic tariff scheduling results in about nine times greater arbitrage revenue compared to fixed rates, 41–58% higher yearly net income, and lower degradation costs. The results confirm that the SLDT is a practical and accurate platform for degradation-aware operational planning in modern smart-grid environments. Full article

An increasing penetration of distributed energy resources and electrification-driven peak demand pose significant challenges to distribution networks, often resulting in voltage violations and congestion. This paper presents a multi-stage optimization framework that enables battery storage unit (BSU) to act as a flexible non-wire alternative to traditional grid expansions conducted by Distribution System Operators (DSO), but also helpful for Transmission System Operators (TSO). The proposed method integrates a mixed-integer planning model with a quadratically constrained, second-order-cone–relaxed, AC optimal power flow to determine the optimal siting and sizing of battery storage. Representative operating days are obtained through clustering, while the operational optimization model evaluates battery participation in energy and reserve markets under network constraints. The value of flexibility the DSO procures from an independently-owned battery storage unit is determined as the opportunity cost of providing this flexibility as opposed to taking part in the fast reserves and day-ahead energy markets. The results obtained offer valuable information when weighing the decision between network expansion and alternative strategies and determine the price of flexibility that the DSO can offer to an independently owned storage unit. The results confirm that battery storage can defer network investments while providing transparent and economically justified flexibility remuneration. The proposed framework is implemented sequentially, with strong coupling between planning and operational stages through physical constraints and economic signals. Full article

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system. Full article

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of day-ahead schedules. This paper proposes a bi-level coordinated scheduling framework that integrates day-ahead mixed-integer quadratic programming (MIQP) with intraday Soft Actor–Critic (SAC)-based correction. In the upper layer, MIQP generates a 24 h baseline schedule subject to unit output limits, mutually exclusive charging/discharging logic, and operational constraints. In the lower layer, SAC performs bounded real-time residual correction for hydropower and battery storage around the MIQP baseline, while a deviation-triggered replanning mechanism forms a closed-loop process of planning, execution, correction, and replanning. Comparative experiments under the tested setting show that SAC achieves better overall performance than Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Typical-day evaluations under dry-, normal-, and wet-season conditions show that, in the selected case studies, the proposed MIQP + SAC framework achieves better performance than standalone MIQP and MIQP-Replan, which refers to a deviation-triggered MIQP re-optimization strategy, in load tracking, PV curtailment reduction, and hydro-storage coordination. These results indicate the effectiveness of the proposed framework for short-term HPBS scheduling under representative operating conditions. Full article

The rapid growth of prosumer photovoltaic installations has introduced significant supply–demand imbalances in modern power grids, motivating the development of energy management systems that can coordinate distributed resources without sacrificing local control responsiveness. This paper presents qToggleEMS, a distributed architecture that combines cloud-resident receding-horizon planning with edge-resident bounded-override control for prosumer sites equipped with photovoltaic generation, battery storage, and grid interconnection. The contribution is positioned at the systems-engineering level: a documented partitioning of responsibilities between a cloud planner (forecasting, price-aware scheduling) and an edge controller (sub-second actuation, autonomous fallback) that preserves planning quality while remaining operational under cloud–edge disconnection. The cloud component, powerHub, is implemented as a set of microservices communicating via MQTT and TimescaleDB; the edge component runs qToggleOS on an ARM single-board computer and accesses inverters directly via Modbus RTU, bypassing manufacturer-provided cloud APIs. The system was deployed at a commercial prosumer site for approximately two months using the prosumer-oriented optimization strategy. Compared with a within-period counterfactual baseline (the cost the site would have incurred under its previous flat-tariff contract), monthly energy costs decreased by 14–15%. An analytical projection of the producer-oriented strategy using historical day-ahead prices from OPCOM PZU suggests a revenue uplift of approximately 23%, pending field validation. Full article

The energy transition of district heating systems in Poland requires the simultaneous consideration of energy efficiency, operating costs, technical feasibility, and local environmental constraints. This study addresses an identified gap in the literature by combining real operational time series from a municipal district heating system with time-resolved market signals and site-specific resource constraints in a single OPEX-based operational screening framework. A case study is conducted for the city of Ustka using a configuration-based comparison of hybrid supply systems that include a gas-fired combined heat and power (CHP) unit, air-source and ground-source heat pumps, thermal energy storage, and a peak-load boiler. The optimisation model was implemented in MS Excel using the GRG Nonlinear algorithm (Solver) and was driven by the district heating operational data for 2021–2022 together with electricity and natural gas prices from the Polish Power Exchange day-ahead market (TGE RDN), evaluated under both hourly and daily settlement assumptions. The results indicate an optimal capacity split of 1.2 MWel/1.3 MWth for the CHP unit and 1.5 MWel/3.0 MWth for the heat pump system, supported by a required peak boiler capacity of 8.23 MWth. Within the adopted OPEX-based assessment, the lowest value of the unit heat generation indicator was obtained for the CHP-led configuration with combined ground-source and air-source heat pumps (38.45–38.55 PLN/GJ). A distinctive element of the study is the explicit verification of whether an operationally favourable configuration remains practically feasible when local resource constraints are considered. The site assessment indicates limited practical feasibility of the borehole heat exchanger at the analysed location in Ustka, showing that the lowest OPEX result should not be interpreted as a final investment recommendation. The study provides a replicable approach for the Polish district heating operators to screen hybrid transition pathways under real market conditions and to avoid technology choices that are favourable in dispatch models but constrained in practice. From a sustainability perspective, the proposed framework supports more energy-efficient, resilient, and locally feasible district heating transition planning in municipal heat systems. Full article

With the large-scale integration of new energy, electric vehicles, and other new loads, disorderly electricity consumption has led to surging peak loads and heightened overload risks for distribution transformers. Particularly in aging, high-density urban areas constrained by the cost and space limitations of upgrading distribution facilities, there is an urgent need to tap into the flexible load control potential of existing power distribution systems to ensure system safety. This paper proposes a multi-timescale cooperative scheduling framework for flexible loads in distribution systems, deeply integrating the dynamic load capacity of transformers with the dispatchable characteristics of a flexible load. First, a day-ahead scheduling layer based on multi-agent reinforcement learning is constructed to optimize electricity plans and smooth peak–valley loads in the distribution system. Second, a dynamic transformer-rating model for distribution transformers is established to uncover their dynamic load capabilities under varying environmental conditions. Finally, an intraday scheduling layer for flexible loads is developed. It dynamically matches the regulation demands of distribution transformers and flexible loads via real-time optimization of consumption strategies to address electricity price fluctuations and user behavior randomness. Case study results demonstrate that the methods described in this paper effectively reduce power load fluctuations, ensuring the safe and stable operation of distribution and power supply systems. Full article