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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]

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]

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]

In the steel industry, the imbalance between fluctuating oxygen demand and stable supply generally results in excessive oxygen emissions and power waste. Independent optimal scheduling of oxygen distribution (OD) or steelmaking-continuous casting (SCC) has limited ability to handle this issue. To address this challenge, we propose an integrated scheduling model that allows for flexible interaction between OD and SCC. We first develop an SCC scheduling model that applies to machine sharing of duplex and conventional smelting, and derive an OD scheduling model. A critical demand interface is further designed to connect the scheduling models of SCC and OD, resulting in a mixed integer linear programming-based integrated scheduling model. Existing research overlooks the probability information of demand uncertainty posed by unpredictable interferences, which limits the ability to handle uncertain demands. To address this limitation, we embed demand probability distribution via a data-driven ambiguity set and upgrade the deterministic integrated scheduling model via two-stage distributionally robust optimization. Our model was tested with real data and resulted in a 15% reduction in total costs compared to independent scheduling of OD and SCC. Additionally, it effectively reduced uncertainty risk to 8.3% with low conservativeness, demonstrating superior performance compared to recent methods. • Integrated scheduling of oxygen distribution and steelmaking-continuous casting. • Interface model connecting oxygen distribution and steelmaking-continuous casting. • A steelmaking-continuous casting scheduling model that handles duplex and conventional smelting processes. • Distributionally robust integrated scheduling that embeds uncertainty probability. [ABSTRACT FROM AUTHOR]

For the scheduling problem of virtual power plants (VPPs), this paper aims to propose a dynamic trigger scheduling strategy that integrates objective grid information and subjective judgment factors affecting VPP scheduling. The strategy design involves setting initial incentive electricity prices and optimizing price adjustment efficiency. Firstly, this paper establishes a reference incentive electricity price model to derive the initial reference electricity price for the dispatching process. Then, a response willingness model is proposed to establish the relationship between electricity price and VPP response, improving the accuracy of the initial reference electricity price and reducing the frequency of subsequent price adjustments in the actual trigger. Finally, this paper utilizes the groundbreaking dynamic trigger method to adjust the price based on the actual VPP response, obtaining the optimal VPP incentive electricity price. We use Matlab to verify this strategy, and the experimental results show that the average response error using this strategy is reduced by 3.33 kW compared to the contrastive literature. Moreover, scheduling task non-completion does not occur in this method. Thus, the accuracy and reliability of the method are verified. • Propose dynamic trigger strategy to consider the subjective judgment factors. • Improve the scheduling economy of VPPs scheduling. • Reduce the time consumption of dynamic triggers by response willingness model. • The problem of individual error and overall error of the model is solved. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively. • Optimal scheduling of a combined cool, heat, and power-based energy system. • Considering commitment scenarios of microgrids in multi-carrier energy markets. • Integrating resilient operational scheduling of multicarrier energy system. • Considering different demand response programs including incentive-based and price-based programs. [ABSTRACT FROM AUTHOR]

Electric power systems (EPSs) and district heating networks (DHNs) are always independently operated and dispatched but also coupled with each other at the interfaces of combined heat and power (CHP) generation, whereas the existing distributed scheduling methods for the integrated electricity and heat system (IEHS) under uncertainty are computationally expensive in practical applications. To handle this problem, this paper proposes a novel decentralized mixed-integer optimization method for robust coordination involving multiple stakeholders. Firstly, a centralized two-stage robust optimization (RO) scheduling model is installed for the IEHS considering the scheduling economy under the nominal scenario and the adjustment feasibility against uncertainty. Secondly, the Fourier-Motzkin elimination equivalently projects the second-stage feasible region of the two-stage RO scheduling model onto the first-stage optimization, thereby producing a concise centralized RO scheduling model in a mixed-integer linear programming (MILP) formulation. Finally, a dual decomposition algorithm derives the decentralized solution to the resulting MILP-type RO model with guaranteed convergence and optimality. This avoids setting up a coordination center for distributed scheduling. Case testing for two IEHSs validates that the computational efficiency of the proposed method is several tens of times speedup than the traditional distributed RO method with guaranteed solution optimality. • A decentralized mixed-integer optimization method is developed for robust electricity and heat scheduling. • The two-stage robust optimization model is equated to a simplified MILP by the feasible region projection. • A dual decomposition algorithm is devised for decentralized computation of the MILP-type robust model. • The convergence and optimality are ensured for the decentralized decomposition of the non-convex problem. [ABSTRACT FROM AUTHOR]

Decarbonisation is driving dramatic growth in renewable power generation. This increases uncertainty in the load to be served by power plants and makes their efficient scheduling, known as the unit commitment (UC) problem, more difficult. UC is solved in practice by mixed-integer programming (MIP) methods; however, there is growing interest in emerging data-driven methods including reinforcement learning (RL). In this paper, we extensively test two MIP (deterministic and stochastic) and two RL (model-free and with lookahead) scheduling methods over a large set of test days and problem sizes, for the first time comparing the state-of-the-art of these two approaches on a level playing field. We find that deterministic and stochastic MIP consistently produce lower-cost UC schedules than RL, exhibiting better reliability and scalability with problem size. Average operating costs of RL are more than 2 times larger than stochastic MIP for a 50-generator test case, while the cost is 13 times larger in the worst instance. However, the key strength of RL is the ability to produce solutions practically instantly, irrespective of problem size. We leverage this advantage to produce various initial solutions for warm starting concurrent stochastic MIP solves. By producing several near-optimal solutions simultaneously and then evaluating them using Monte Carlo methods, the differences between the true cost function and the discrete approximation required to formulate the MIP are exploited. The resulting hybrid technique outperforms both the RL and MIP methods individually, reducing total operating costs by 0.3% on average. • Compare reinforcement learning and mixed-integer programming for solving unit commitment. • Applied to low carbon power systems with high renewable forecast uncertainty. • MIP outperforms RL, progress is needed in RL scalability and reliability. • A new hybrid methodology is proposed, using RL to warm start the stochastic MIP. • This hybrid method outperforms both RL and MIP individually. [ABSTRACT FROM AUTHOR]

A flexibility estimation model is proposed to evaluate the immediate aggregate flexibility response in a day-ahead scheme. The proposed model schedules a set of appliances and calculates the aggregated flexibility according to the energy and flexibility prices. The temporal granularity of the problem can be adjusted to evaluate the flexibility for the next 15 min or 4 h, using an alternative flexibility scenario approach to evaluate immediate flexibility available. The alternative flexibility scenario is a limited number of time step used to evaluate whether the estimated flexibility is available for a defined time window in each time step. New flexibility constraints were introduced to evaluate the flexibility and the rebound effect for air conditioners, pool pumps, and electric water heater, according to the alternative flexibility scenario. The model was tested under different energy price schemes and flexibility requirement durations to observe how the price signals influenced the flexibility offered. The results show how a model that considers extended flexibility, even if it does not consider the total duration requirement, can offer a more accurate flexibility response than a flexibility estimation without an extended flexibility response. • Introduces an alternative flexibility scenario to evaluate flexibility changing the appliance scheduling per time block. • Evaluates flexibility for multiple energy appliances as the maximum aggregated flexibility offered for continuous time blocks. • Formulation of appliance models with flexibility parameters for day-ahead approaches. • Integration of flexibility expressions and rebound effects on flexible appliance models. [ABSTRACT FROM AUTHOR]

• Providing a RA three-layer strategy for DA scheduling of an AND consisting of multiple MGs. • Reducing the daily expenses of ADN through the design of a dynamic-tariff DRP. • Improving the technical and economic metrics of the ADN by unlocking the potential of SBs and EV fleets. • Improving the equilibrium point of the DA scheduling of MGs through the game theory and the P2P power sharing. The proliferation of the number of Smart Buildings (SBs) and the fleet of Electric Vehicles (EVs) in Distribution Systems (DSs) makes the need for new strategies to coordinate them with microgrid (MG) scheduling inevitable. Therefore, this article proposes a three-layer risk-averse game theoretic-based strategy to coordinate SBs and EV fleets with MGs scheduling. In the first layer of this strategy, a Demand Response Program (DRP) is designed for SBs where dynamic incentive tariffs are calculated based on the consumption pattern of subscribers. Then, in the second layer, the scheduling of SBs and EV fleets is done in a decentralized space and considering their participation in the designed DRP. Eventually, in the third layer, the operators of MGs have received the power exchange information of SBs in order to carry out their scheduling in accordance with it. In this layer, the Day-Ahead (DA) scheduling of MGs and DS is done through the implementation of a cooperative game theory. Fluctuations of uncertain operational parameters such as load demand, radiation and wind are embedded in the model by scenario-based technique where a Risk-Averse (RA) strategy is adopted to manage them. Running the proposed three-layer strategy on a 69-node DS containing four MGs showed that this strategy can use the potential of SBs and EV fleets to improve the voltage characteristics in the high-demand period and reduce total daily costs by 13.66% with designing a dynamic-tariff DRP. Moreover, the results reveal that MGs using the Peer-to-Peer (P2P) power exchange option have not only reduced the power losses in the system but also reduced the total daily costs by about 8%. [ABSTRACT FROM AUTHOR]

• The AES power system intelligent energy scheduling environment is built. • The BI-LSTM-Att action network is propose by combining BI-LSTM with attention mechanism. • The DQN-CE algorithm is proposed by improving the DQN algorithm. • The experiments on an all-electric ferry is conducted using a variety of methods. At present, Ship energy scheduling based on deep reinforcement learning (DRL) is an important research direction in which Deep Q learning algorithm (DQN) has been successfully applied in the field of energy scheduling. In terms of the shortcomings of DQN in all electric ships(AES) energy scheduling, such that, the insufficient performance of using multiplayer perceptron(MLP) as action network and the degradation of convergence performance due to over estimation of Q value, in this paper, we propose a DQN cross entropy(DQN-CE) energy scheduling algorithm combing bi-directional LSTM and attention mechanism(BI-LSTM-Att). Instead of using MLP, firstly, we use BI-LSTM-Att as action network of DQN, and then an improved DRL algorithm called DQN-CE is proposed, that is, we add cross-entropy loss of predicted action of the next time for the target network and the action network to DQN. Finally, we conduct simulation experiments on an all-electric ferry, and the experimental results show that the energy scheduling algorithm proposed in this paper reduces the economic consumption by 4.11%, increases the utilization rate of energy storage system by 24.4%, and reduces the exploration time of agent training by 31.3% compared with the original DQN energy scheduling algorithm. Finally, the effectiveness and superiority of the algorithm were further verified in the new case study. [ABSTRACT FROM AUTHOR]

Steady-state voltage security region (SVSR) is an important criterion that can intuitively and effectively evaluate the power system's operation security and overall safety margin. However, there is no literature on its application to electric vehicle (EV) proliferation. Since EV introduces dynamism and uncertainties to the grid, most existing indices cannot assess the grid accurately. Given this, we propose a load margin index (LMI) based on SVSR considering uncertainties. This is used to assess the impact of EV proliferation via a bi-layer optimal EV scheduling model. The upper layer of the model solves a dispatching plan for EV aggregators (EVA) by maximizing the LMI and minimizing the deviation between the upper and lower layer power schedules. The lower layer models each EV's charging/discharging strategy by minimizing the EV's operating cost, the EV user's target residual energy, and the deviation between the lower and upper layer schedules. We also employ a real-time scheduling plan to further adjust the day-ahead scheduling due to forecast errors. A case study based on the IEEE-33 node distribution network verifies the practicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A framework model of scheduling air separation units unifies multiple load-adjustment types. • The framework is applied to oxygen system scheduling, benefiting energy conservation and emission reduction. • Due to the multi-period, the pipe pressure calculated by the oxygen scheduling model deviates from the actual value. • A boundary compensation method accurately handles the pipe pressure deviation, ensuring system security. Steel production requires enough oxygen, which is produced by cryogenic air separation (ASU) with a large load, high power, and slow adjustment. Facing complex oxygen demands, effective ASU scheduling can reduce energy waste and has great potential for low-carbon development. Current ASU scheduling models have only a sole load-adjustment type. To address the limitation, a novel scheduling framework model is researched for ASUs with various load-adjustment types, and is applied to an oxygen system scheduling mode1 for steel enterprises. In addition, current multi-period oxygen scheduling models have an issue that time discretization leads to oxygen pipe pressure deviation between model calculation and practical operation and impacts on system safety. Therefore, a novel method to compensate for pressure boundaries is studied to precisely handle the deviation. Some contrast tests concerning various load-adjustment types of ASU are conducted with simulative and real data. The simulative tests indicate that the framework can both unify multiple adjustment types (MAT) and flexibly migrate among various ASUs. In the real scenario, the oxygen scheduling model with the MAT-ASU avoids oxygen emission and reduces comprehensive cost (including electricity consumption and oxygen release loss) of the manual scheduling by 24%. The MAT enhances the response ability to complicated demands, thereby performing better to optimize the comprehensive cost. Though smaller pipe volume or larger discrete period size increases the pressure deviation, the compensation method perfectly ensures safe system operation. [ABSTRACT FROM AUTHOR]

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques. • A novel rTPNN-based Forecast Embedded Scheduling (rTPNN-FES) is presented. • rTPNN-FES simultaneously forecasts and schedules household loads. • Significant improvement is achieved in forecasting compared to the state-of-the-art. • Near optimal scheduling is achieved with much lower computation time. [ABSTRACT FROM AUTHOR]

Optimally scheduling alkaline electrolyzers (AELs) in a hydrogen-based microgrid (HBM) can greatly unleash the operational flexibility of the HBM. However, existing scheduling strategies of AELs mostly utilize a simplified AEL model, which ignores the nonlinear coupling of electric-hydrogen-thermal sectors, violating the AEL's security constraints thereby making the scheduling scheme infeasible. This paper proposes an improved model predictive control (MPC) based optimal scheduling framework which incorporates a scheduling correction algorithm into the basic MPC structure. This framework is utilized for implementing economic and resilient scheduling of an HBM under normal and emergency conditions, respectively. With the scheduling correction algorithm, this framework can be formulated into a computationally efficient mixed-integer linear programming, meanwhile guaranteeing the solutions strictly satisfy the security constraints of hydrogen facilities (i.e. AEL and hydrogen tank). Case studies are conducted based on real operating data of a Danish energy island Bornholm. The results demonstrate that the proposed scheduling scheme under normal conditions can contribute to significant comprehensive benefits from the daily operation cost saving of 68%, computational time saving of 98%, and satisfying the security constraints of hydrogen facilities, compared to previous scheduling strategies. Besides, it sharply reduces load shedding under emergency conditions by proactively allocating distributed energy sources in the HBM. • A novel economic and resilient scheduling scheme is presented for microgrids. • An improved electrolyzer model is built to precisely predict its state evolution. • A computationally efficient solving algorithm is proposed via an improved MPC. • The scheduling cost is saved by 68%, within the security of hydrogen facilities. • The scheduling scheme maintains the supply for critical loads during extreme events. [ABSTRACT FROM AUTHOR]

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory. To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori , employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology. In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating. We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available. • Time series aggregation for capacity expansion planning models. • A posteriori: adapted to underlying planning model. • Chronology-preserving: able to model storage technologies. • Identifies and includes relevant extreme events. • Unlocks use of longer samples and climate model data in planning studies. [ABSTRACT FROM AUTHOR]

• This paper firstly considers the route scheduling of the shuttle tankers for the continuous production scenario. • A novel multi-visit-multi-voyage model is formulated that enables the shuttle tankers to conduct the inventory-dependent route scheduling. • The routing related variables are constructed in a reduced dimension to narrow the searching space for the solver. The deep-sea oil exploitation leads to the growing utilization of the Floating Production Storage and Offloading (FPSO), where the crude oil is processed and stored until it can be transferred to a shuttle tanker. The conventional scheduling method of one shuttle tanker serving a fixed FPSO is challenged by the increasing numbers and capacities of the deep-sea FPSOs. This paper proposes a collaborative scheduling method by pooling the transportation capabilities of the shuttle tankers and conducting inventory-dependent route planning. The inventory headroom of the FPSO is taken as a key variable for the route planning of the shuttle tankers. A multi-visit-multi-voyage model is constructed to schedule the optimal routes for the shuttle tanker fleet in the multi-voyage time period. Case studies demonstrated that by implementing the collaborative scheduling, the proposed model could achieve 11.14% transportation cost saving in the planning period when compared with the conventional shuttle tanker scheduling model. By proper combining the shuttle tanker types, further cost savings can be obtained while using less shuttle tankers to execute oil transportation. [ABSTRACT FROM AUTHOR]

• Define and quantify hydropower flexibility in power regulation and frequency response. • Flexibilities are coordinated in day-ahead scheduling of hydro-wind hybrid power system. • Benefits and costs of hydropower providing flexibility. • Suggested schemes weighing benefits and costs under different capacity ratios. Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability. [ABSTRACT FROM AUTHOR]

• The proposed approach considers multi-uncertainties from load and generation sides. • Leverage deep reinforcement learning to automatically handle multiple uncertainties. • Build an energy/matter co-transfer model, i.e. hydrothermal simultaneous transmission. • Simulation tests were carried out on an actual island IES in North China. Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit's output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. [ABSTRACT FROM AUTHOR]

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders' operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations for e-mobility ecosystems. The proposed three-layer joint DRCC framework formulates the interactions of the stochastic behaviour of the stakeholders in an uncertain environment with unknown probability distributions. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. An iterative process is proposed to solve the problem using joint DRCC formulation. To achieve computational tractability, the second-order cone programming reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of the temporal correlation of uncertain PV generation on CSs operation is considered in the formulation. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, USA. The simulation results show the necessity and applicability of such a scheduling framework for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61. In addition, the choice of confidence level significantly affects the cost and revenue of the stakeholders as well as the accuracy of the schedules in real-time operation, e.g., for a low-risk case study, the total net cost of EVs increased by 247.3% compared to a high-risk case study. Also, the total net revenue of CSs and retailers decreased by 26.6% and 10.6%, respectively. • Mutual interactions between stochastic parameters of stakeholders are investigated. • PV temporal correlations has be formulated in the optimization problem. • An exact second-order cone programming reformulation is presented to improve accuracy. • By increasing the confidence level, the cost of EVs and charging stations increased. • Number of EVs with constraint violation decreased by increasing confidence level. [ABSTRACT FROM AUTHOR]

In this paper, distributed model predictive control (MPC) based energy scheduling problem is presented for islanded multi-microgrids. The objective is to achieve supply–demand balance in an individual microgrid through energy coordination and reduce the battery degradation for its extended cycle life. In order to achieve desired objective, the battery state of charge is taken as zone control optimization problem and new slack variable is introduced as optimized variable to restrict the battery state of charge in optimal range with considering battery degradation. Furthermore, accelerated distributed augmented Lagrangian (ADAL) based distributed coordination strategy is presented, which can improve power supply reliability through coordinated cooperation among MGs for energy exchanges. The system framework is modeled as mixed-integer dynamic model which switches between different operating conditions. A mixed-integer quadratic programming approach is addressed to solve the MPC optimization problem. The effectiveness of the proposed scheme is validated through comparative performance with existing model. Finally, convergence analysis and comparative fast convergence performance of the proposed scheme is provided. • This paper proposed energy scheduling for isolated multi-microgrids (MMGs). • Distributed Zone-MPC based EMS strategy is proposed for individual MG. • Battery life is improved through Zone constraint MPC. • ADAL method is utilized for distributing the interconnected optimization problem. [ABSTRACT FROM AUTHOR]

• A novel stochastic-robust approach is developed for energy communities. • Peer-to-peer exchanges and collective assets are considered and analysed. • The problem is solved using a novel three-stage procedure. • Operating cost is incremented 77$ when the degree of robustness grows. • Collective storage systems help to reduce unserved demand and wasted generation. Energy communities enable effective coordination among prosumers on pursuing collective targets. This paper focuses on isolated 100% renewable communities, involving individual (controllable appliances and small generators) and collective (wind generators and battery banks) assets. To effectively coordinate the agents involved in these structures, advanced energy management strategies are necessary. This work develops a three-stage day-ahead scheduling strategy for isolated 100% energy communities, involving peer-to-peer transactions among prosumers. The different uncertainties involved are incorporated through a novel stochastic-robust formulation, that results in a computationally tractable optimization framework. To validate the new model, a case study on a six-prosumer benchmark community is analysed. Results reveal the importance of collective assets and peer-to-peer exchanges among prosumers as well as the effectiveness of the developed formulation. The role of batteries is also discussed, helping to reduce the total unserved energy and operating cost by 20% and 19%, respectively, as well as enabling a more efficient use of wind energy. The impact of robustness is also studied, incrementing the expected importable energy by 28% compared to the deterministic case, while the exportable energy from prosumers is notably reduced by 40%. However, uncertainty-aware strategies have a direct impact on operational costs, incrementing the expenditures by 37% when uncertainties are considered. [ABSTRACT FROM AUTHOR]

As the competitive advantages of electric vehicles, both in terms of operating costs and eco-friendly characteristics have gained attention, the demand for electric vehicles has increased, and studies for efficiently charging electric vehicles are being actively conducted. Previous studies have mainly focused on scheduling one electric vehicle visiting a charging station or scheduling multiple electric vehicles in a centralized execution method. However, a decentralized execution method that can schedule multiple vehicles according to their status is more suitable in a realistic smart grid charging environment that requires quick decisions. Therefore, we propose a multi-agent deep reinforcement learning approach with a centralized training and decentralized execution method that can derive charging scheduling for each electric vehicle. Computational experiments show that the proposed approach shows desirable performance in minimizing the operating cost of electric vehicles. • We address the charging scheduling problem of multiple EVs in the smart grid. • Multi-agent deep reinforcement learning algorithms are applied to the problem. • The proposed algorithms outperformed the existing charging heuristic algorithm. • We obtained a reasonable EV charging method while minimizing the total cost. • The method can be extended to other microgrid charging scheduling problems. [ABSTRACT FROM AUTHOR]

• Federated power plant for preserving the privacy and willing. • Two-stage scheduling framework of HVACs. • Distributed scheduling of HVACs based on transactive energy. • Distributed control of HVACs via ADMM. This paper focuses on the optimal scheduling of heating, ventilation and air-conditioning systems (HVACs) to address the fluctuation problems caused by the increasing renewable energy integration with power systems. A two-stage framework is proposed to schedule HVACs. In the first stage, a federated power plant (FPP) is employed to regulate HVACs to serve the grid. A transactive energy (TE) market is built within FPP to schedule HVACs to preserve customers' privacy and willingness, which is characterized by the Stackelberg game and a distributed method is provided to solve this problem. In the second stage, HVACs are required to track the target power when the TE market is cleared. Considering that HVACs are complicated systems with multiple coupled internal components and are difficult to be optimized by the centralized method, the alternating direction method of multiplier (ADMM) method is utilized to control HVACs in a distributed way for better-optimized scheduling. Case studies show that the proposed distributed methodology can schedule HVACs in a private, effective and accurate way. [ABSTRACT FROM AUTHOR]

• Time spent at home increased 1.2–1.9 h per day during the pandemic. • 1-member households spent less time at home. • 2 + member households spent more time at home. • Office and kitchen space usage increased during the pandemic. • Changes to occupancy have strong implications on home energy use. Many aspects of the daily lives of those living in the United States were substantially impacted by the COVID-19 pandemic in the year 2020. A broad diversity of measures was implemented to curb the spread of the virus, many of which included adjustments to where and how people worked, went to school, and otherwise conducted their daily lives compared to pre-pandemic times. This has impacted how residential buildings are used, how much time people spend in their homes, and as a result, how much energy these buildings consume. The main objective of this study is to analyze, at a national scale, the differences in the occupancy schedules and activities conducted in homes in the U.S., as compared to pre-pandemic. 15 years of American Time Use Survey and Current Population Survey data, from 2006 to 2020, was used in this study to analyze the occupancy schedules for both pandemic (2020) and pre-pandemic (2006–2019) times. These impacts were also analyzed with respect to variables including, weekday/weekend, month of the year, age of the occupants, household income, and household size. The impact of the pandemic on occupant schedules were most substantial in the initial months, whereas as the months progressed, these occupancy profiles slowly changed. Across 2020, people spent, on average, 8 % more time (1.9 h) in their home on weekdays, and 3–6 % (1.2 h) on weekend days. The percentage of time spent for different activities and locations within homes were also studied. For 1-member households, their time spent at home decreased whereas for 2-, 3-, and 4- member households, they spent more time at home. Overall, people spent around 45% more time doing office- and work-related activities at home compared to pre-pandemic, which is likely due to increased remote working and schooling. This research helps to improve the understanding of the occupancy presence and absence profiles in U.S. residential buildings due to the pandemic and provides new insights as to modified profiles for researchers, building designers, and policy makers. [ABSTRACT FROM AUTHOR]

Under the background of the popularization of renewable energy sources and gas-fired domestic devices in households, this paper proposes a joint load scheduling strategy for household multi-energy system (HMES) aiming at minimizing residents' energy cost while maintaining the thermal comfort. Specifically, the studied HMES contains photovoltaic, gas-electric hybrid heating system, gas-electric kitchen stove and various types of conventional loads. Yet, it is challenging to develop an efficient energy scheduling strategy due to the uncertainties in energy price, photovoltaic generation, outdoor temperature, and residents' hot water demand. To tackle this problem, we formulate the HMES scheduling problem as a Markov decision process with both continuous and discrete actions and propose a deep reinforcement learning-based HMES scheduling approach. A mixed distribution is used to approximate the scheduling strategies of different types of household devices, and proximal policy optimization is used to optimize the scheduling strategies without requiring any prediction information or distribution knowledge of system uncertainties. The proposed approach can handle continuous actions of power-shiftable devices and discrete actions of time-shiftable devices simultaneously, as well as the optimal management of electrical devices and gas-fired devices, so as to jointly optimize the operation of all household loads. The proposed approach is compared with a deep Q network (DQN)-based approach and a model predictive control (MPC)-based approach. Comparison results show that the average energy cost of the proposed approach is reduced by 12.17% compared to the DQN-based approach and 4.59% compared to the MPC-based approach. [ABSTRACT FROM AUTHOR]

• A hybrid-driven DEH is established to model the ICES under off-design conditions. • Data-driven PR and BPNNs methods are used to correct nonlinear equipment efficiency. • A two-stage scheduling method based on the hybrid-driven DEH is proposed. • RO strategy is adopted to enhance the adaption to supply–demand uncertainties. • The solution speed, accuracy and economy of scheduling schemes are improved. The integrated community energy system (ICES) is an effective means to promote the synergies among multiple energy carriers. However, the off-design performance of equipment challenges the accurate and economical scheduling of the ICES. To solve this problem, a two-stage scheduling method for the ICES based on a hybrid mechanism and data-driven model is proposed in this paper. Combing the mechanism energy hub (EH) model with a data-driven efficiency correction model, a hybrid-driven dynamic energy hub (DEH) with variable equipment efficiency is built first. The EH describes the multi-energy coupling relationship; the embedded efficiency correction model adopts data-driven approaches of polynomial regression (PR) and backpropagation neural networks (BPNNs) to accurately extract nonlinear characteristics of equipment efficiency. On this basis, a two-stage scheduling model for the ICES is developed. In the day-ahead stage, the PR method is applied to calculate equipment efficiency which varies with load rate. The day-ahead scheduling model is established with the aim of minimizing the operating cost. In the intraday stage, considering the effects of load rate, temperature, and atmospheric pressure, the BPNNs method is employed to further correct equipment efficiency using the latest data. Furthermore, a rolling optimization (RO) strategy is used to address the uncertainties of equipment efficiency and load demand to improve the accuracy and economy of the scheduling scheme. Case studies demonstrate that the proposed method can improve the solution speed and accuracy of the scheduling model, and enhance the operating economy of the ICES. [ABSTRACT FROM AUTHOR]

• A new structure of off-grid integrated energy system (OIES) for an isolated renewable energy hydrogen refueling station (HRS) is proposed. • A mixed integer quadratic constrained programming (MIQCP) model of the OIES is built. • These three loads are obtained by using the authoritative building model to simulate the operation in EnergyPlus software. • The optimal sizing and scheduling results and the sensitivity analysis results are presented numerically and visually. When designing an isolated renewable energy hydrogen refueling station (HRS), it is important to consider the electrical, heating and cooling demands of the supporting building as it is crucial for maintaining the stable HRS operation. Therefore, this paper proposed a new structure of off-grid integrated energy system (OIES) for an isolated renewable energy HRS, which can meet the hydrogen load of the market and the three kinds of loads of the supporting building. The OIES in question consists of wind generators, PV panels, batteries, electrolyzers, heat storage tanks, hydrogen tanks, and absorption chillers. A mixed integer quadratic constrained programming (MIQCP) model of the OIES is built based on the minimization of the total life cycle cost (TLCC). It is built to obtain the optimal solution for sizing and the equipment units' scheduling strategy. To accord with the actual situation, the electrical, heating, and cooling loads of the supporting building in the simulation are obtained by the EnergyPlus software. Furthermore, the 'community' hydrogen demand is obtained by the HOMER software. The results indicate that the proposed structure meets a wide array of energy demands and is more sensible solution when compared to other configurations with one component less. In addition, sensitivity analyses of the influence of economic data, meteorological data, and hydrogen load on TLCC are performed. These results show that the capital and replacement cost of the PV panels are the major economic factors influencing the TLCC. They also confirm that the PV is the system's main component due to a high correlation between the hourly hydrogen demand per year and the hourly solar radiation per year. Lastly, the effects of meteorological data and hydrogen load are also quantitatively analyzed. [ABSTRACT FROM AUTHOR]

• A novel data-driven surrogate is developed for chance constraint reformulation. • Wind curtailment dispatch is explicitly modeled in chance constraints. • A correction strategy is proposed to ensure modeling accuracy. • The proposed approach is effective under arbitrary distribution assumptions. Chance-constrained optimization (CCO) has been widely used for uncertainty management in power system operation. With the prevalence of wind energy, it becomes necessary to consider wind curtailment as a dispatch variable in CCO. However, the wind curtailment will cause an impulse for the uncertainty distribution, yielding challenges for explicit modeling of chance constraint. In this paper, a novel data-driven framework is developed to handle this issue. By modeling the wind curtailment as a cap enforced on the wind power output, the proposed framework constructs a data-driven surrogate to describe the relationship between wind curtailment and the tightening boundary of chance constraints. This allows us to reformulate the CCO with wind curtailment as a mixed-integer second-order cone programming (MI-SOCP) problem. An error correction strategy is developed by solving a convex linear programming (LP) to improve the modeling accuracy. The notable characteristic of the proposed method is that the chance constraints with wind curtailment can be explicitly and accurately handled without any distribution assumptions, which provides great convenience for the power dispatch under a high percentage of renewables. Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO. It shows that the proposed method outperforms existing methods by producing more economic and secure solutions for the chance-constrained energy and reserve scheduling problem under uncertainties. [ABSTRACT FROM AUTHOR]

Participants in energy auction markets must simultaneously decide on the quantity and price of their offers while attempting to ensure bid acceptance through the market clearing process. To participate profitably in these markets, energy resource aggregators require accurate knowledge about the costs and quantity of electricity that can be produced and consumed, i.e., knowledge about scheduling decisions. When scheduling and bidding decisions are combined, complex interdependencies arise. Integrated scheduling and bidding with pay-as-bid remuneration has long been treated as a non-linear problem. However, by sampling stochastic parameter distributions, a practical and useful mixed-integer linear programming formulation for the integrated problem can be derived. Within the sampled distribution, the integrated model produces a priori optimal expected profit decisions. Recently, such a linear formulation for a price-taker with pay-as-bid remuneration was proposed for the first time. Our contribution completes that formulation to guarantee non-anticipativity, standardizes it with uniform remuneration, and extends it to integrated scheduling and bidding of energy and control reserve. An additional focus is placed on reserve provision through storage plants. A novel reserve scheduling model with reserve availability guarantees is introduced. This allows for leveraging the full flexibility of storage plants and, simultaneously, reveals information about the direction of reserve power flows. The integrated model is validated in a case study based on real-world data by investigating trade-offs for scheduling and bidding decisions through analysis of the Pareto front of three competing objectives. The results show that a large proportion of bids are placed at price levels with high acceptance probability, which reduces risk. Decreasing storage utilization rates increase risk and reduce solution robustness because of lost flexibility, and result in increased reserve market participation. [ABSTRACT FROM AUTHOR]

• Multi-stage and multi-zone distribution network and green HRSs expansion planning. • Investment and scheduling of HRSs, RESs, and distribution networks are coordinated. • Green HRSs with the capability of onsite H 2 production and storage are modelled. • Water electrolyser powered by wind/solar to locally produce hydrogen is suggested. • Determine optimal timing, location, and sizing for HRSs, RESs, and network assets. In line with the growing pressures on implementing zero-carbon emission policies and the implementation of hydrogen in the transport sector, energy markets are experiencing inevitable transformation and interactions. Since fuel cell electric vehicles have been attracting considerable attention, the production and supply of renewable hydrogen through hydrogen refuelling stations (HRSs) are of great importance. The growing energy demand, inappropriate siting and sizing of HRSs, and high penetration of distributed renewable energy sources (RESs) make power distribution network planning very challenging. This paper proposes an integrated multi-stage and multi-zone expansion planning framework to coordinate the investment and scheduling of HRSs, wind and solar energy sources, and the distribution network. To model the green HRSs, water electrolysers powered by renewable electricity and storage tanks to locally produce and store hydrogen are suggested. The objective of the proposed problem is to minimise the investment, operation, emissions, and maintenance costs of network's assets, RESs, and HRSs. The RESs expansion scheme comprises the installation of two different groups; the former pertains to distributed renewable sources installed over the network while the latter is integrated into HRSs. Case studies are conducted on 6-node and real Australian 100-node distribution networks. The results show the effectiveness of the proposed model in terms of optimal timing, sizing, location, and operational schedules of HRSs, RESs, and distribution network's assets. [ABSTRACT FROM AUTHOR]

• A framework is proposed to describe the generic natural disaster modelling process. • The disaster scenario database is constructed to provide data for resilience research. • Reusable impact-increment database is built to accelerate resilience assessment. • Component-level indices are proposed for targeted resilience enhancement. A planning-oriented resilience assessment and enhancement approach is proposed that can efficiently deal with multi-type natural disasters. A unified disaster modelling framework is proposed to extract key information from various potential disaster scenarios, thus forming a disaster scenario database. The impact-increment-based enumeration method is applied, and a reusable impact-increment database is established to speed up the assessment process. The reusable database is also utilized to calculate component-level resilience indices and economic indices, so as to make enhancement strategies against potential disasters within planning time scale. Resilience assessment on an integrated electricity-gas system in Taiwan's coastal seismic statistical zone shows that the proposed method can significantly improve the computational efficiency as compared to existing methods. Numerical results indicate that the resilient planning considering the diversity of natural disaster types comprehensively improves the system resilience, which means it is not only concerned with the system performance under a single type of disaster. In addition, the most suitable resilience enhancement scheme with insufficient funds shall be developed according to the economic indices, instead of the component-level resilience indices that cannot balance the resilience enhancement effect with the implementation cost. [ABSTRACT FROM AUTHOR]

• An energy-oriented selective maintenance policy (ESMP) is developed. • ESMP policy is applied to series–parallel multi-unit systems under sustainable manufacturing. • Preventive maintenance and preventive replacement can be optimized comprehensively. • Optimal maintenance schemes are obtained under limited maintenance resources. • ESMP policy achieves a significant improvement of energy efficiency in batch production. With the industry sustainable development and increasing awareness of energy conservation, many manufacturing enterprises prefer to develop the operation and maintenance (O&M) with high production throughput and low energy consumption. In reality, many manufacturing systems are required to conduct a sequence of predefined missions with finite breaks between any two consecutive missions. To successfully complete the next mission production, maintenance actions are arranged and performed on machines during each scheduled break. In this paper, an energy-oriented selective maintenance policy (ESMP) for series–parallel systems is investigated. At each break, multiple maintenance actions with different impacts on machine degradation are available under limited maintenance resources. To obtain the throughput-and-energy based maintenance scheme, we first model the system energy efficiency based on system throughput and energy consumption. Then, we integer the energy efficiency modeling, production throughout analysis, and selective maintenance scheduling into an optimization model. And the model objective is to find the appropriate maintenance action for each machine at each break subject to cost and duration constraints. Numerical examples have been addressed to demonstrate the performance and adaptability of our proposed ESMP in long-term selective maintenance scheduling. Finally, a comparative analysis with traditional reliability-oriented policy shows the significant improvement of energy efficiency. [ABSTRACT FROM AUTHOR]

• A novel uncertainty-aware day-ahead scheduling tool for microgrids considering response fatigue is developed. • The novel methodology presents a modular and linear formulation, being thereby solvable by conventional solvers. • Various types of demand response programs are modelled and analysed. • Uncertainties and response fatigue are jointly treated by an original information gap decision theory-based methodology. • A benchmark case study serves to show the capabilities and effectiveness of the developed method. The implantation of demand response programs may be unsuccessful due to a variety of reasons. One of the most important is the so-called response fatigue, which refers to the discouragement experienced by consumers when they received an excessive number of signals from the operator. This circumstance is, however, typically ignored in energy management tools of electrical energy systems. To solve this issue, this paper proposes an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids based on information gap decision theory, which incorporates additional constraints to bound the duration of demand response signals. Thereby, the harmful effects caused by response fatigue are lessened. The developed optimization problem is formulated as a Mixed-Integer-Linear programming, which is solvable using standard solvers and versatile enough to be adapted to different system layouts. A benchmark case study serves to show the effectiveness of the developed methodology to manage with uncertainties, while the effect of response fatigue in consumers is bounded to acceptable thresholds. As a sake of example, the developed methodology is able to determine a scheduling plan with a total renewable generation 50% lower compared with the deterministic case, while the total demand is overestimated by ∼20%, in which the effect of response fatigue is kept within acceptable bounds yet. Accurateness and efficiency of the new proposal are also checked by making a comparison with other uncertainties modelling. [ABSTRACT FROM AUTHOR]

Extreme events are featured by high impact and low probability, which can cause severe damage to power systems. There has been much research focused on resilience-driven operational problems incorporating mobile energy storage systems (MESSs) routing and scheduling due to its mobility and flexibility. However, existing literature focuses on model-based optimization approaches to implement the routing process of MESSs, which can be time consuming and raise privacy issues since the requirement for global information of both power and transportation networks. Furthermore, a real-time automatic control scheme of MESSs has become a challenging task due to the system high variability. As such, this paper develops a model-free real-time multi-agent deep reinforcement learning approach featuring parameterized double deep Q-networks to reformulate the coordination effect of MESSs routing and scheduling process as a Partially Observable Markov Game, which is capable of capturing a hybrid policy including both discrete and continuous actions. A coupled transportation network and linearized AC-OPF algorithm are realized as the environment, while the internal uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through learning procedure. Extensive case studies including both 6-bus and 33-bus power networks are developed to evaluate the effectiveness of the proposed approach. Specifically, a detailed comparison between different multi-agent reinforcement learning and model-based optimization approaches is conducted to present the superior performance of the proposed approach. • A resilience driven coordination of mobile energy storage systems is proposed. • The coordinated problem is formulated as a Partially Observable Markov Game. • A parameterized multi-agent deep reinforcement learning approach is proposed. • Both transportation and power networks are considered. • A linearized AC-OPF capturing network and technical constraints is utilized. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hierarchical optimization framework that couples the balancing problem of a community network with building energy management (BEM) to form an integrated model capable of capturing potential joint-flexibilities of connected buildings while co-optimizing the operation schedules of community-operated assets. The proposed framework comprises a community-level power flow-based model to solve the energy procurement problem of the community and a building-level physics-aware simulation model to estimate a set of day-ahead aggregate load scenarios with potential joint-flexibilities. At the building-level, a least-cost multi-objective optimization provides a sequence of optimal temperature setpoints for all thermal zones that is fed into an accurate Gradient Boosting Machine (GBM) to estimate heating, ventilation, and air conditioning (HVAC) load trajectories while considering human comfort, occupancy patterns, building thermal response, and intraday electricity prices. The community-level model co-optimizes day-ahead schedules of shared distributed energy resources (DERs) and electric vehicle (EV) chargings while directing the building cluster to adapt the joint-flexibilities to balance the community network under different operation conditions. Finally, a test building cluster located in Qatar University is investigated through several case studies. The simulation results reveal the model's practicality in fully capturing the joint-flexibilities of the building cluster for an adaptable community operation which cuts the operation cost by 21.04%. • A tiered two-level scheduling strategy is proposed for a smart building community. • Building-level physics-aware simulation model optimizes zonal temperature setpoints. • Community-level model co-optimizes schedules of shared assets and joint-flexibilities. • Integrated model balances community network while satisfying power flow constraints. • Adaptable community operation with joint-flexibilities cuts operation costs markedly. [ABSTRACT FROM AUTHOR]

• Propose a risk-averse day-ahead generation scheduling approach for hydro–wind–PV complementary systems. • Design a two-layer nested optimization framework to solve the generation scheduling model. • Proposed approach effectively satisfies the steady requirement of power delivery. • Formulated generation schedule achieves balance between risk and benefit. Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system's generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants. [ABSTRACT FROM AUTHOR]

Operational flexibility is required in power systems to mitigate load-generation imbalances. Inflexibility either results in infeasible scheduling or shift resources from their economic operating point. System operators must estimate flexibility requirement, assess its availability from committed resources, and take corrective measures to handle upcoming inflexibility events. Various metrics are integrated with economic dispatch to quantify different facets of flexibility — ramp, power, and energy. Consideration of all three facets is essential for its adequate assessment, but is often neglected in literature and requires an in-depth investigation. Further, existing literature hardly consider resources' day-ahead scheduling decisions while evaluating flexibility for real-time operations. This results in erratic assessment of available flexibility. In this context, the paper proposes a comprehensive metric to quantify flexibility in terms of ramp, power, and energy insufficiency by simultaneously considering their system-wide requirement and availability. A Resource Flexibility Index based on operating range and ramping capability of resources is proposed for accurate indication of available flexibility. The proposed metric is integrated with real-time stochastic multi-interval scheduling framework that considers day-ahead operational constraints. Netload forecast and associated uncertainty are characterized using Long Short-Term Memory and Markov Chain Monte Carlo techniques. Results highlight that the flexibility index is proportional to system's netload variability handling capability and average inflexibility can be reduced up to 97% with the utilization of emerging resources and ramp products. The proposed tools are of value to power system planners and operators to manage netload intermittency. • Flexibility quantification considering system-wide requirement and its availability. • Resource Flexibility Index based on operating range and ramping. • Machine-learning based methods to forecast netload and generate scenarios. • Multi-interval scheduling to dispatch resources and integrate flexibility metric. • Integrating flexible resources and ramp products reduce inflexibility. [ABSTRACT FROM AUTHOR]

The natural gas system and the electric power system are becoming increasingly coupled in consideration of more interdependent components, e.g., natural gas-fired units, in the systems. Energy supplies for these two systems need to be dispatched economically from their own perspectives when these two systems belong to different owners. This paper investigates the best response-based individually look-ahead scheduling for the natural gas system and the electric power system. The optimal scheduling strategies for these two systems are the best responses of the opposite sides, and the best response of each side is achieved by an iterative best-response-search approach. Each best-response-search process is established as a bilevel mixed integer linear programming model, in which the upper level aims to minimize each side's operational cost constrained by on–off states of generators/lines/gas sources, and the lower level aims to minimize the total energy consumption cost constrained by the operational constraints in consideration of the scheduling of generators/lines/gas sources of two systems in the upper level. To deal with solving difficulty caused by binary variables in the lower level, the original bilevel mixed integer linear programming model is converted into a constrained single-level formulation, which is solved by a decomposition algorithm based on an iterative column-and-constraint generation method. Two cases validate the model and the algorithm, and the costs with the best response-based model and the joint optimization-based model are compared. • Best response-based individually look-ahead scheduling of the power system and the natural gas system is presented. • The best response for each side is achieved by an iterative best-response-search approach. • Each best-response-search process is established as a bilevel mixed integer linear programming model. • The bilevel mixed integer linear programming model is converted into a constrained single-level model. • Costs with best response-based and joint optimization-based models are compared. [ABSTRACT FROM AUTHOR]

• A decentralized scheduling method is proposed for the optimal operation of integrated community energy systems. • The decentralized optimization problem is solved via an improved consensus-based alternating direction method of multipliers (ADMM). • Better convergence performance can be achieved by pre-defining the decreasing direction of residuals and dynamic updating the step size. • Less computation expense can be achieved under not only the normal operation but also the information loss situation. To transform the original centralized scheduling framework of integrated community energy systems (ICES), a decentralized scheduling method is proposed for the optimal operation of ICES. Firstly, linearized mathematical models of the electric distribution system and natural gas distribution system are established to eliminate the nonconvexity and nonlinearity of the multi-energy systems. Meanwhile, an improved energy hub model is proposed by introducing the state variable, which is able to obtain the optimal solution with better accuracy and higher efficiency. Secondly, a decentralized scheduling framework is developed via an improved consensus-based alternating direction method of multipliers (ADMM). By introducing several coupling links as consensus variables, the optimal operation of the ICES is decoupled according to the physical interactions among each sub-system, in which the original model can be solved parallelly in a decentralized manner. Only the consensus variables are exchanged among sub-systems so that the privacy and confidential items of each sub-system operated by different entities can be ensured. Finally, two different ICES test cases with different system scales are utilized to demonstrate the effectiveness of the proposed method, which is able to provide scheduling scheme same as the centralized method. Dynamic step size modification is further considered in the consensus-based ADMM. By updating the step size during each iteration, the computation expense of optimization process is reduced with less consuming time, iteration and apparent oscillation, by which the convergence performance of ADMM is improved with higher computation efficiency. [ABSTRACT FROM AUTHOR]