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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]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [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]

• The flexibilities of multiple links are utilized with economic performance improved. • The adaption to uncertainties is enhanced by the MPC-based rolling optimization. • The scheduling strategy with reservation margins is robust for real-time errors. Community integrated energy system (CIES) couples multiple energy forms and links, it is important to cope with the inadequacy of flexibility for realizing efficient utilization and reliable supply. Based on the detailed modeling of central energy station, district heating network (DHN) and building loads, a unified scheduling model is established by fully considering the coupling characteristics of the sources, the networks, and the loads. Meanwhile, the flexibilities from adjustable capacity of thermal storage device and the indoor temperature on the demand side, as well as the energy storage of DHN, are utilized to improve the economy. Furthermore, a model predictive control (MPC) based robust scheduling strategy is proposed to maintain and utilize CIES flexibility for enhancing the uncertainty adaptability. The scheduling framework consists of rolling optimization and robust constraint generation. The rolling optimization schedules the system with a better adaptation to flexibility demands, and the robust constraints generate adjustable margin for building demands by modifying the upper/lower and ramping limits. After the linearization of nonlinear terms in the model, case studies are conducted based on the data of a typical day in summer. The results show that the unified source-network-load scheduling strategy can give full play to the flexibilities of different energy links with a better operation economy. Additionally, the MPC-based optimization can well adapt to the rolling forecasting uncertainties, and the schedule generated by advance reserving means has stronger robustness for real-time errors. [ABSTRACT FROM AUTHOR]

Highlights • An integer linear programming technique is applied to optimise cleaning strategy. • The model is robust and applicable to any concentrating solar field shape and size. • Clustering analysis is applied to reduce problem dimensionality. • Local heuristics are used to improve routing sub problems. Abstract Soiling of heliostat surfaces due to local climate has a direct impact on their optical efficiency and therefore a direct impact on the productivity of the Solar Power Tower plant. Cleaning techniques applied are dependent on plant construction and current schedules are normally developed considering heliostat layout patterns, providing sub-optimal results. In this paper, a method to optimise cleaning schedules is developed, with the objective of maximising energy generated by the plant. First, an algorithm finds a cleaning schedule by solving an integer program, which is then used as a starting solution in an exchange heuristic. Since the optimisation problems are of large size, a p-median type heuristic is performed to reduce the problem dimensionality by clustering heliostats into groups to be cleaned in the same period. [ABSTRACT FROM AUTHOR]

Highlights • A day-ahead scheduling strategy is proposed with detailed device modeling. • The effects of multiple thermal storage devices on flexibility are fully utilized. • The unit commitment constraints in practical operation are well considered. • The optimal scheduling model is transformed into a MILP formulation by linearization. Abstract The operation of community integrated energy systems (CIESs) is challenging because it is necessary to couple energy production, conversion and consumption on the end-user side. Multiple energy demands need to be fed simultaneously with the goal of economy and reliability. This paper formulates the optimal scheduling problem based on a real community integrated energy system in China. The system has multiple chillers (i.e. three ground source heat pumps, two conventional water-cooled chillers and two double-duty chillers) and two types of thermal storage devices (i.e. two cold water tanks and an ice-storage tank). Based on detailed device modeling, a community integrated energy system operation strategy considering unit commitment is proposed, and then is transformed into a mixed-integer linear programming model by linearization of nonlinear items. Case studies are conducted based on the data for a typical day in summer. The results show that the proposed strategy can utilize the flexibility of the energy storage devices and realize an economic and reliable operation of the community integrated energy system by coordinating various energy devices. The strategy can also reduce the startup/shutdown frequency of chillers significantly. [ABSTRACT FROM AUTHOR]

Highlights • An asynchronous production line modeling based on finite state machine is formulated. • The problem of maximizing production rates under power constraints is considered. • Provide a novel framework to manage production lines during demand response intervals. • A constrained local search heuristic is developed to find near-optimal schedules. • Simulation results show considerable improvement in production rates. Abstract Energy efficiency in factories is a new paradigm arising with Demand Response (DR) programs that enables energy providers to ask their clients to reduce their power consumption for a given time. In this paper, we consider demand side management of an industrial customer who owns asynchronous production line systems. Our aim is to help the customer achieve a good trade-off between production rates and power consumption during DR events. To attain this objective, we develop a framework named DR-Mgmt to schedule activities of machines on production lines so that the number of simultaneously working machines is maximized while satisfying DR constraints. Our framework first models activities of a production line using a new approach based on temporal deterministic finite station machine concept, where each state represents machine status (working/idle) and transitions capture temporal changes. Then, the problem of finding an optimal schedule from all feasible schedules is handled by selecting the optimal set of state transitions. We adapt the well-known local search heuristic to find near-optimal transitions. Numerical results show a significant benefit of our approach on production rates during DR intervals. Compared to other approaches, the proposed framework performs best and improves production rates up to 70% in some cases with higher total power consumption. We also validate our work by a real case study. [ABSTRACT FROM AUTHOR]

Highlights • New formulations enhance the modeling of certain characteristics or processes. • Integration into the electricity market and solar self-consumption management. • Real-world case study for the test-bed plant representative of an industrial cluster. Abstract The energy hub concept and modeling methodology are widely employed tools for solving resource conversion and storage scheduling problems. For instance, industrial clusters might benefit from determining the suitable time to operate their facilities and to sell electricity to the public power grid, according to legal, economic or environmental factors. In this paper, novel elements are introduced in order to more accurately represent real plants and to reduce the amount of decision variables. The major innovation is to consider devices consuming a resource which is not related to the quantity of output produced, by attaching binary decision variables to certain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources within the system instead of employing a variable for each branch between the components. The third innovation consists of an additional vector to express the amount of output resources sold from the energy hub, including constraints for those resources which are exported and imported through the same medium. An extended energy hub model is first proposed and then applied to a real plant example, including multiple and heterogeneous resources and performing a comparison between days with different demands, weather conditions and electricity prices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and therefore validate the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • Stochastic day-ahead microgrid management algorithm considering uncertainties in PV, load and temperature. • Quantification of the effectiveness of demand side management by implementing thermal models and thermal comfort constraints. • Comparison of comfort constraint violations and annual operational costs of a stochastically managed microgrid. • Comparison of deterministic and stochastic day-ahead management strategies. Abstract Microgrid operations are challenging due to variability in loads and renewable energy generation. Advanced tools capable of taking uncertainty into account are essential to maximize microgrid benefits when operating microgrid owned DERs. This paper proposes a novel optimization model for day-ahead economic dispatch of flexible resources within a microgrid environment, considering uncertainty of PV and loads.This model is conceived to support the microgrid supervisory control layer, providing a security-constrained day-ahead strategy to operate three types of microgrid flexible resources: PV, electric storage and controllable loads. The work presented in this paper introduces a novelty in microgrid operations by presenting a stochastic version of the day ahead scheduling of microgrid DERs to deal with uncertainties associated with PV, load and temperature while considering microgrid network limits and end-user comfort as optimization constraints. An annual analysis quantifies the benefits of to the microgrid-owner of a stochastic formulation over a deterministic one both in terms of ensuring end-user comfort and decreasing operation costs. [ABSTRACT FROM AUTHOR]

The increasing challenges to the grid stability posed by the penetration of renewable energy resources urge a more active role for demand response programs as viable alternatives to a further expansion of peak power generators. This work presents a methodology to exploit the demand flexibility of energy-intensive industries under Demand-Side Management programs in the energy and reserve markets. To this end, we propose a novel scheduling model for a multi-stage multi-line process, which incorporates both the critical manufacturing constraints and the technical requirements imposed by the market. Using mixed integer programming approach, two optimization problems are formulated to sequentially minimize the cost in a day-ahead energy market and maximize the reserve provision when participating in the ancillary market. The effectiveness of day-ahead scheduling model has been verified for the case of a real metal casting plant in the Nordic market, where a significant reduction of energy cost is obtained. Furthermore, the reserve provision is shown to be a potential tool for capitalizing on the reserve market as a secondary revenue stream. [ABSTRACT FROM AUTHOR]

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [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]

In order to determine the optimal day-ahead scheduling schemes of the integrated community energy system (ICES), a two-stage multi-objective scheduling method (TMSM) was proposed, which consists of a multi-objective optimal power flow (MOPF) calculation stage and a multiple attributes decision making (MADM) stage. Firstly, the electric distribution network, the natural gas network and the energy centers (ECs) of the ICES were modelled. Secondly, five typical indices are considered to characterize the operation of ICES, namely the operation cost ( OC ) and total emission ( TE ) of ICES, the power loss ( PL ) and sum of voltage deviation ( SVD ) of electric distribution network, the sum of pressure deviation ( SPD ) of natural gas network. In order to tackle the computation problems resulted by the increasing number of objectives, the dimension reduction of objectives is employed. The indices of OC and TE are selected based on the analytic hierarchy process (AHP) method and set as the objectives at the MOPF calculation stage. Thirdly, all the five indices are considered during the MADM stage to determine the final day-ahead scheduling schemes from the alternative solutions obtained in MOPF. Numerical studies demonstrate that the TMSM is able to provide flexibility for the operation of ICES. The determined optimum day-ahead scheduling schemes are capable of satisfying and balancing operational needs in aspects of security, economy and environmental friendliness. [ABSTRACT FROM AUTHOR]

This work analyses the economic and technical impact of local reactive power provision in grid-connected microgrids with distributed energy resources. Costs of reactive power provision by photovoltaic systems and battery energy storage systems are explicitly formulated and an objective function incorporating the costs is proposed. The advantage of the proposed objective function is validated by comparing it with other objective functions frequently employed in the literature. From various case studies, the extent of economic and technical benefits of local reactive power provision for the microgrid is established. Subsequently, the technical and economic competitiveness of reactive power provision using inverter-based distributed energy resources are compared against those using switched capacitors. Extensive sensitivity analyses are performed to determine the scenarios in which one technology is more competitive than the other. Inverter efficiency has been identified as the most important parameter for reactive power provision from distributed energy resources while electricity price is the most crucial factor for switched capacitors’ competitiveness in producing reactive power. [ABSTRACT FROM AUTHOR]

This paper reports the development of a two-level optimization methodology to help design a tri-generation system for a given district which satisfies the heating, cooling, and hot water demands and at the same time, minimize the annual total costs and CO 2 emissions. An optimization methodology is proposed and tested on a virtual district with eight buildings where three of them can host the district technologies including heat pump, gas engine, and lake cooling. Within the building, some backup technologies may be implemented including an air/water heat pump, a water/water heat pump, a boiler, and electric chillers. Analysis of the Pareto optimal frontier results in several distinct groups of configuration based on the selected district conversion technologies and their capacities. Solution to the sub-problems including design and operation of the district energy system is carried out by applying a Mixed Integer Programming (MIP) technique. Several different clusters are defined and studied regarding the cost and CO 2 emission. A reference configuration is defined for the purpose of comparison in which electricity is supplied by the grid, heating and hot water by a boiler, and cooling by an electric chiller. Compared to this configuration, the best solution with respect to CO 2 emissions causes 59% emission and 75% cost of the reference configuration. In this case, 53% of the total cost is associated with the initial investment cost while the rest 47% is associated with the operational cost. The optimal configuration with respect to the annual costs causes 86% more emission than the reference configuration and 38% less annual costs. In this case, 22% of the total cost is associated with initial investment cost while 78% of the total cost is associated with the operational cost. Implementation of a two-pipe system instead of a four-pipe system results in nearly 5% reduction in total annual cost. [ABSTRACT FROM AUTHOR]

This work is focused on the optimal management of electricity and heat generation and demand in microgrids. The objective of the proposed mathematical model is to adjust energy and heat availability profiles resulting from the use of renewable energy sources and flexible energy and heat demands. The optimisation of the resulting short-term problem is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model to minimise the operational cost of the microgrid. Delays in the energy demands are allowed to tackle flexible demand profiles, under penalties in the objective function. An additional characteristic was the consideration of non-constant profiles in the considered tasks. Also, this model takes into account eventual interruptions in the tasks, applying penalties in the economic objective function. The main decisions to be made includes the schedule of tasks, as well as energy and heat generation levels, purchases from and exportation to the power grid, and storage levels. [ABSTRACT FROM AUTHOR]

We present an power systems optimisation model for national-scale power supply capacity expansion considering endogenous technology cost reduction (ESO-XEL). The mixed-integer linear program minimises total system cost while complying with operational constraints, carbon emission targets, and ancillary service requirements. A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time. Two cost learning curves for the different power technologies are derived: one assuming local learning effects, the other accounting for global knowledge spill-over. A piece-wise linear formulation allows the integration of the exponential learning curves into the ESO-XEL model. The model is applied to the UK power system in the time frame of 2015 to 2050. The consideration of cost learning effects moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. In addition, the maximum capacity build rate parameter influences the share of power generation significantly; the possibility of rapid capacity build-up is more important for total system cost reduction by 2050 than accounting for technology cost reduction. [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]

In this paper, a comprehensive strategy for the scheduling of unbalanced microgrids is proposed. The microgrids are characterized by the presence of a variety of distributed resources, such as data centers, electric vehicles, and distributed generation systems. The operation strategy is based on the solution of a multi-objective optimization problem, whose objective functions are related to specific services and requirements of the microgrid, such as cost minimization, power quality improvements, and energy savings. Particular attention is paid to the unbalances presence in the microgrid with the aim to outline how the proposed strategy is able to significantly reduce them taking contemporaneously into account other important economic and technical objectives. An analysis of the objectives and the different operational perspectives also is proposed, which allows us to exploit the effectiveness of the multi-objective approach. The results of numerical applications are presented to show the effects of the considered strategy in terms of achievable advantages. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) is an important technology option for reducing CO 2 emissions into the atmosphere. The most commercially viable way to deploy CCS on a large scale is via coupling with enhanced oil recovery (EOR) operations. These operations allow the reduction of CO 2 emissions through geological sequestration, coupled with generation of additional revenues through increased oil production as a result from CO 2 re-injection through EOR. EOR also enables both CO 2 utilization and storage (CCUS) as a carbon management strategy with long CO 2 storage life. In practice, planning EOR operations takes into account mass balance and temporal aspects of a given site. When multiple oil reservoirs are involved, it is necessary to allocate the available CO 2 supply and schedule suitable timing for EOR operations for these reservoirs. CO 2 allocation and scheduling are thus important aspects in maximizing the economic benefits that arise from EOR operations. In this work, a mixed integer linear programming (MILP) model is developed to address CO 2 allocation and scheduling issues for EOR operations. A discrete-time optimization approach is developed to consider both economic discounting and reservoir depletion. Two illustrative case studies are solved to illustrate the model. [ABSTRACT FROM AUTHOR]

In this paper, we present a planning approach for reducing the impact that failure events in the natural gas (NG) network impose on the electricity market operation. For this purpose, an efficient computational methodology based on the Monte Carlo sequential simulation approach is developed, in order to realistically simulate the annual operation of the NG network and deduce the failure events at the connection nodes of the NG-fired power plants. The failure events are then fed into a mid-term scheduling model based on the day-ahead electricity market clearing algorithm, in order to quantify their impact on electricity prices, unit commitment and dispatch, and the energy mix. An appropriate methodology on the location selection of new storage capacity in the NG network is also described, and a case study based on the combined Hellenic electric power system and NG network is used as a test-bed for evaluating the alternative locations. [ABSTRACT FROM AUTHOR]

A new hierarchical distributed architecture for control and scheduling of Heating, Ventilation, and Air Conditioning (HVAC) operations in multi-zone commercial buildings is proposed. Each zone has an associated Zone Module responsible for determining its cooling needs which are specified in terms of tokens . Tokens are a surrogate for the amount of chilled air supplied to each zone in a service interval. Token requests are computed by Zone Modules using occupancy forecasts, thermostat information, all available sensor data, and weather forecasts to capture the cooling energy necessary to meet user-specified temperature constraints. A Central Scheduler gathers token requests from all Zone Modules and allocates tokens to minimize the energy consumption of the chiller and fans. Zone Modules maintain and update local thermal models based on new measurements and recompute token requests in a model predictive control framework. This distributed control/scheduling approach is robust to varying environmental conditions and user constraints. It is scalable to realistic buildings with a large number of zones, and has low deployment cost. The proposed architecture can readily accommodate chiller efficiency through Coefficient of Performance (COP) specifications, as well as constraints on cooling air mass flow rates, fan capacities, duct pressure distribution, and damper opening constraints. Simulation studies reveal that the proposed approach suffers modest performance loss as compared with centralized non-linear scheduling strategies. These centralized strategies, however, are not scalable to buildings with 300+ zones and suffer prohibitive deployment costs. [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]