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The development of 100% renewable energy (RE) systems provides a viable solution for achieving the global target of carbon neutrality. To support the reliable and economical operation of RE-based local energy networks, this paper presents a joint scheduling model for grid-scale RE generation and hydrogen-based flexible loads. The direct load control (DLC) through hydrogen-electrical microgrids is analytically modeled for leveraging the intrinsic flexibility of demand-side multi-energy synergy. To handle the uncertainty and volatility of RE generation, a risk-averse stochastic programming method with the receding-horizon mechanism is developed. Also, the power balancing cost in scheduling objectives is represented as a conditional value-at-risk (CVaR) measure to control the risks of fully RE supply. Case studies on an exemplary RE system confirm the effectiveness and economic benefits of the proposed method. The hydrogen-enabled DLC can largely mitigate the supply–demand mismatches, which shows a great potential to facilitate 100% RE scenarios. • A joint scheduling model of grid-scale renewable energy generation and hydrogen-based flexible loads is proposed to achieve carbon neutrality. • Direct load control through hydrogen-electrical microgrids is analytically modeled and implemented. • A risk-averse stochastic receding-horizon scheduling method is developed to handle the uncertainty of renewable energy generation. • Power balancing cost is represented as conditional value-at-risk (CVaR) to control the operational risks. • Results of case studies indicate the feasibility and cost-effectiveness of methods application for a 100% renewable energy scenario. [ABSTRACT FROM AUTHOR]

Air-conditioners (ACs) can fully utilize the inherent characteristics of storing heat/cold in demand response (DR), achieving peak load shifting and renewable energy consumption. However, traditional control strategies based on compressor ON/OFF state require retrofitting of the internal components of ACs, making it challenging to implement directly through ACs' infrared control protocol. Given the widespread availability of infrared temperature-set-point (TSP) adjustment functions in existing ACs, this paper proposes a day-ahead optimal scheduling strategy of aggregate ACs under TSP control. Based on the second-order equivalent thermodynamic parameter (ETP) model, the TSP adjustment characteristics are considered to establish the equivalent energy storage (EES) model of ACs. On this basis, taking the overall optimization objectives of power system into account, the optimal scheduling model of aggregate ACs under TSP control is established to solve collaborative optimization problems for power generation and consumption. Case study demonstrates that, compared to optimal scheduling models based on traditional EES model, the proposed model exhibits superior peak load shifting effects and allows for smooth control of ACs through the issuance of control commands. • An optimal scheduling model of aggregate air-conditioners based on equivalent energy storage model is established. • Discrete temperature-set-point control realized through air-conditioners' infrared control protocol. • The aggregate air-conditioners' energy storage capacity and their potential in demand response are enhanced. [ABSTRACT FROM AUTHOR]

To make full use of the flexible adjustment capability of DC tie-lines in multiarea AC/DC systems and to coordinate the generation resources and load demand of multiarea AC/DC systems, this paper presents an intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems based on analytical target cascading (ATC). To avoid repeated adjustment and overadjustment of DC tie-lines after wind power integration, a two-stage rolling coordinated scheduling model for the area subsystem based on model predictive control (MPC) is proposed. The two-stage rolling coordinated scheduling method takes into consideration the influence of the predicted value in the future finite time domain and the latest measured DC tie-line power on the current scheduling state. Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed that takes into consideration the DC tie-line adjustment constraints and the area coupling constraints. The optimal scheduling model for the upper-level system formulates the two-stage DC tie-line plan for the multiarea AC/DC system, and the two-stage rolling coordinated scheduling model of the area subsystem solves the subproblems of the generation plan for each area subsystem in a parallel manner considering the area coupling constraints. The proposed method can achieve intraday two-stage decoupling scheduling of multiarea AC/DC systems and promote the cross-area consumption of large-scale wind power through flexible adjustment of the DC tie line. This approach also reduces the communication burden between the area subsystems and ensures the efficiency of the solution algorithm. • Based on analytical target cascading technology (ATC), we integrate the hierarchical control structure of decomposition-coordination into the model predictive control (MPC) method to achieve decentralized dispatch for multiarea AC/DC systems. • A two-stage rolling coordinated scheduling model for the area subsystem considering the area coupling constraints based on the MPC is proposed. • Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed. • Flexible adjustment of the DC tie-line and decoupling scheduling of multiarea AC/DC systems are achieved. • The hierarchical optimal scheduling model reduces the communication burden between the subsystems and ensures the efficiency of the solution algorithm. [ABSTRACT FROM AUTHOR]

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ -constraint method is employed and decision makers' attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations. • Multi-objective sizing and scheduling method for the hybrid renewable system is proposed. • Mathematical metrics to measure wind and solar power complementarity is provided. • A complementarity rate of 0.19 can be reached with 100% reliable service and no curtailment. • Integrating wind power into the hydro-PV system can reliably serve 4.11% more load. • An optimal rather than minimal level of the renewable power curtailment is more efficient. [ABSTRACT FROM AUTHOR]

Distributed energy systems (DES) have garnered global attention as a promising solution for the expansion of renewable energy sources. However, stochastic uncertainty in renewable sources poses significant challenges in the collaboration optimization of system design and operation. In this study, a comparison analysis was conducted to assess the effectiveness of the conventional distribution-based scenario generation (DS) method for stochastic optimization of a distributed energy system in residential buildings. The results revealed that the DS method inaccurately captured extreme scenarios and exhibited limitations in operation optimization, leading to significant performance evaluation bias. To address these challenges, a novel stochastic optimization approach was developed based on error-based scenarios and a day-ahead and real-time dynamic scheduling strategy (ES-DRS). This approach incorporated solar energy prediction errors to more accurately characterize extreme scenarios, while also considering dynamic dual-scale meteorological boundary condition for rolling operation optimization. Furthermore, the buffer storage and coverage periods in ES-DRS were investigated and discussed during dynamic scheduling. Results demonstrated that when the buffer storage and coverage period were set at 36.22 kWh in summer and 24 h, respectively, the DES with ES-DRS achieved optimal multi-objective performance. This resulted in an annual total cost of 3.21 × 104 USD and CO2 emission of 5.82 tons, representing reductions of 26.45% and 61.06%, respectively, compared to the conventional strategy. Overall, this research contributes to advancing uncertainty analysis and scenario-based optimization in DES, highlighting the potential benefits of adopting the ES-DRS approach to maximize overall performance from both economic and environmental perspectives. • Error-based scenarios with a day-ahead and real-time dynamic optimization approach. • Both extreme scenario characterization and operation optimization are addressed. • The buffer storage and coverage period are used to cope with uncertain perturbations. • The proposed strategy reduces the cost by 26.45% compared to the conventional one. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan. • The scheduling model of VPP consider capacity degradation of ESSs, carbon trading, and DR strategies. • A lease mechanism for coal-fired units based on the combination of carbon credits and prices is proposed. • This paper proposes ISBDR strategies for reduceable loads. • The ESSs capacity degradation model adopted takes into account DOD and SOC. • A multi-time scale scheduling strategy of VPP is adopted to deal with multiple uncertainties. [ABSTRACT FROM AUTHOR]

With deepening decarbonization and increased Renewable Energy Sources (RESs) integration, the power system's inertia has declined, affecting the network's ability to balance power at the distribution level. Concurrently, the proliferation of prosumers presents a regulatory opportunity for Distribution System Operators (DSOs), despite the complexity introduced by their high number and varied behaviors. This paper introduces a new co-scheduling model optimizing prosumers' capacities through Virtual Power Plants (VPPs) in local networks, enhancing DSO oversight and facilitating prosumer participation in regulation markets. The proposed model concurrently schedules energy provision alongside voltage and frequency regulation capacities. Recognizing prosumers' behavioral uncertainties, Data-Driven Distributionally Robust Optimization (DDRO) is employed to ensure adequate capacity for VPP engagement. Importantly, the paper outlines a mechanism allowing DSOs to partner with multiple privately-owned VPPs, ensuring privacy through an adaptive Alternative Direction Method of Multipliers (ADMM) method. This method avoids the exchange of sensitive information, ensuring confidentiality and scalability. Consequently, VPPs can proficiently manage scheduling and communicate their regulation capacities. The operator then dispatches control signals based on regulation needs and network flow. Results from the IEEE 33 bus test system confirm the model's efficacy in enhancing voltage support and frequency regulation, and generating revenue for both VPPs and prosumers. • This paper proposes a new co-optimization strategy for distribution-level VPPs. • A distributed coordination approach between DSO and VPPs is presented. • An adaptive consensus ADMM is developed to model the communications. • The uncertain nature of prosumer behavior is addressed by a DDRO model. • The effectiveness of the developed approaches is extensively illustrated. [ABSTRACT FROM AUTHOR]

In steel industries, oxygen demands are generally uncertain due to unpredictable disturbances in steel production, which seriously impacts system safety. Conventional static oxygen scheduling exhibits excessive conservativeness and limited robustness. Thus, a Multi-Stage Adaptive Robust Scheduling method for oxygen systems is proposed to adaptively address uncertain demands over time. To achieve this, an oxygen system scheduling model that integrates multiple load-adjustment modes of the air separation unit is first established to improve responsiveness to oxygen demands. Then, the scheduling time horizon is divided into multiple stages. For each stage, a data-driven uncertainty set is constructed to capture the most critical uncertainty distribution interval with low conservativeness. In addition, computationally efficient recourse decisions are designed with a few additional variables. These designs upgrade the deterministic oxygen scheduling model to a computationally tractable Multi-Stage Adaptive Robust Scheduling model that can dynamically adjust evaporators and emissions to address observed uncertainty promptly. The superiority of the proposed model is demonstrated using real data of an oxygen system. Compared to conventional oxygen scheduling, the adjustable robust scheduling improves the ability to handle uncertainty at least three-fold and saves cost, thereby enhancing system robustness and while reducing conservativeness. The designed recourse decisions greatly improve solution efficiency by two orders of magnitude, and the constructed data-driven uncertainty set also benefits robustness and cost optimality. • Adjustable multi-stage robust oxygen scheduling, adapting to demand uncertainty. • An oxygen scheduling model with multiple air separation unit adjustment modes. • Computationally efficient recourse decisions, depending on a recent time period. • Data-driven uncertainty sets, accurately capturing the densest distribution region. [ABSTRACT FROM AUTHOR]

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. • Detailed power and water operations are included in a hydrothermal scheduling model. • It incorporates short-term storage, reserve requirements, and irrigation effects. • The uncertain hydro inflows are represented by vector autoregressive models. • A calibration method captures the main effects induced by operational constraints. • Extensive experiments are analyzed on a detailed Chilean hydroelectric representation. [ABSTRACT FROM AUTHOR]

One of the fundamental concerns of distribution companies (DISCOs) is how to satisfy security requirements under uncertain conditions and real-time contingencies. To address this concern, it is vital to employ operational flexibility levers aimed at mitigating congestion and retaining optimal reserve capacity, particularly in critical feeders. Increased interaction between DISCOs and virtual energy storage systems (VESSs) offers an unparalleled opportunity to unlock the energy flexibility of distribution grids at the local level without increasing operational complexity. According to the state-of-the-art, models that tap the economic performance of VESSs while respecting the flexibility and security setpoints adopted by DISCOs in a distributed manner have remained scarce. This study goes beyond by developing a flexibility-oriented decision-making strategy as a single-leader multi-followers stochastic model; in which a multi-district DISCO involves the flexibility setpoints and congestion management strategy in the market participation plan of each VESS locally to enhance the energy flexibility of each district. In the upper level, DISCO aims to deal with stressful situations in real-time sessions by (i) determining the minimum reserve capacity requirements in each district to be provided by VESSs with respect to the existing uncertainty sources and the importance of each district, and (ii) alleviating the congestion in critical feeders to release the feeder capacity for deployment of the available reserve power promptly. In the lower level, each VESS makes optimal decisions on power and reserve bidding in day-ahead, reserve, and real-time markets that are affected by the flexibility services required by the DISCO. Inspired by the activity schedule of different users, it also examines how demand response programs can affect DISCO's ability to unlock consumer-side flexibility in realizing congestion management strategies. The developed bi-level scheduling model is first reformulated as single-level mathematical program with equilibrium constraints (MPEC) by employing Karush–Kuhn–Tucker (KKT) optimality conditions and then linearized into a mixed-integer linear program (MILP). The proposed strategy is validated through extensive simulations conducted on the modified IEEE 33-bus test system consisting of three districts with different users. Furthermore, DIgSILENT PowerFactory is used to verify the feasibility of the proposed model under contingency cases and to provide insights for DISCOs to control multiple VESSs and capacity-correlated uncertainties. • A local optimization strategy is proposed for coordinated operation of VESSs and multi-district DISCO. • The flexibility and security requirements of DISCO are reflected in the market-oriented strategy of VESSs. • The reserve setpoints are adopted in each district to achieve a local power balance under stressful conditions. [ABSTRACT FROM AUTHOR]

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller. • A physically consistent deep learning (PCDL) model for thermal dynamic prediction. • A PCDL-based community energy dispatching and indoor comfort control framework. • Novel case study results compared to alternative data-driven models. [ABSTRACT FROM AUTHOR]