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Highlights • An analytical framework for quantifying the flexibility of ADNs was proposed. • Flexibility of network transmission was analysed with the limits of physical network. • The benefits of controllable resources for flexibility improvement were explored. Abstract With the integration of high shares of distributed generators (DGs), it is increasingly difficult to cope with the various uncertainties and puts forward higher requirements for the operational flexibility in active distribution networks (ADNs). Controllability of ADNs has been significantly improved by the application of energy storage and power electronic devices. However, due to the difficulties in effective coordination of various controllable resources, the controllability cannot be fully translated into the system flexibility. In this paper, an analytical framework for quantifying the flexibility of ADNs is proposed, including the quantification of node flexibility, the matching of system flexibility, and the flexibility of network transmission. The proposed framework provides a novel perspective of flexibility to reinterpret operation issues of distribution networks. As the power imbalance and voltage deviation are even more severe caused by the high shares of DG integration, the indexes of flexibility from magnitude, frequency and intensity dimensions are deteriorated in ADNs operation. Through the spatial and temporal regulation of power flow, various controllable devices, such as soft open points (SOPs) and energy storage system (ESS), can effectively mitigate the power imbalance and voltage deviation to improve the indexes of system flexibility. Thus, under the unified analytical framework, the potential benefits of system controllability are fully utilized to provide effective countermeasures for the flexible operation of ADNs. Finally, case studies are performed on the modified IEEE 33-node system to quantify the operational flexibility of ADNs and verify the flexibility enhancement brought by controllable resources. [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]

There is an increasing awareness of resilience for active distribution networks (ADNs) as extreme fault conditions pose threats to the reliable operation of power supply. Islanding operation of ADNs with distributed generators (DGs) and energy storage system (ESS) can significantly serve the critical electricity demands and improve the power supply reliability. Considering the characteristics of DG, ESS and load, a time-series islanding partition model of ADNs is established based on soft open point (SOP). The proposed model exploits the benefits of operational flexibility provided by SOP to improve the level of load recovery. It further takes into account the coordination of the control strategies among regulation devices as well as the failure duration in island partition, while satisfying a variety of operational constraints. Specifically, a mixed-integer nonlinear programming (MINLP) is employed to solve the proposed model effectively. Finally, case studies on IEEE 33-node and IEEE 123-node test feeders verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the integration of high shares of distributed generators (DGs), both volatile DGs and demand-side resources make it increasingly difficult to cope with the various uncertainties and put forward a higher requirement for the operational flexibility in active distribution networks (ADNs). The highly integrated distribution-level power electronic devices represented by soft open point (SOP) significantly benefit the operation of ADNs. Due to the accurate and rapid power flow control provided by SOP, various flexible resources can be coordinated in the spatial and temporal aspects, which effectively increases the DG penetration of ADNs. In this paper, the multi-dimensional characteristics of distribution system flexibility are analysed and the maximum DG hosting capacity is adopted as the index to evaluate the flexibility brought by SOP. The flexibility evaluation model is further proposed to quantify the benefits of SOP-based flexible resources, such as conventional SOP, multi-terminal SOP and SOP with energy storage. Then, the original non-convex nonlinear model is converted into an effectively solved second-order cone programming (SOCP) model using convex relaxation. To tighten the deviation of convex relaxation to the predefined accuracy, a difference-of-convex programming (DCP)-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

An energy management system for stand-alone microgrid composed of diesel generators, wind turbine generator, biomass generator and an ESS (energy storage system) is proposed in this paper. Different operation objectives are achieved by a hierarchical control structure with different time scales. Firstly, the optimal schedules of the diesel generators, wind turbine generator, biomass generator and ESS are determined fifteen minutes ahead according to the super short-term forecast of load and wind speed in the optimal scheduling layer. Comprehensive analysis which takes the uncertainty of load and wind speed into account is conducted in this layer to minimize the operation cost of the system and ensure a desirable range of the state of charge of the ESS. Secondly, the operation points of each unit are regulated dynamically to guarantee real-time power balance and safety range of diesel generation in the real-time control layer, based on which the response capability when suffering significant forecast deviation and other emergency issues, e.g. sudden load-up can be improved. Finally, the effectiveness of the proposed energy management strategy is verified on an RT_Lab based real-time simulation platform, and the economic performances with different types of ESS are analyzed as well. [ABSTRACT FROM AUTHOR]

Highlights • An interactive mechanism is proposed for smart distribution networks with microgrids. • The interaction process is formulated as Stackelberg game under a market framework. • The uncertainty of microgrids is handled by two-stage robust method. • A collaborative strategy is established to promote the trading among microgrids. Abstract This paper proposes a two-level interactive mechanism to address the day-ahead scheduling optimization of smart distribution networks with multi-microgrids. Each microgrid realizes the economic dispatch in the lower level interactive model, where the uncertainties caused by renewable energy integration and loads as well as the coordination of controllable distributed generator, energy storage and demand response are handled through a two-stage robust model. In the upper level interactive model, the distribution network operator (DNO) guarantees the operational quality by considering the power flow constraints and decides the transaction price and quantity with each microgrid based on the solutions of the lower level problem, to minimize the operation cost. The interaction process is formulated as a Stackelberg game, in which the trading strategies of coupled microgrids, acted as followers, are influenced by price incentive mechanism of DNO and affect the decision of DNO in turn. A collaborative strategy promoting the internal power trading among microgrids is also presented to deal with the market power of DNO, such that the participating microgrids can benefit from the internal trading and further reduce the cost. Numerical cases on an improved IEEE 33-bus system show the effectiveness of the proposed model and solution strategy. [ABSTRACT FROM AUTHOR]

Long-term energy storage (LTES), such as hydrogen storage, has attracted significant attention due to its outstanding performance in storing energy over extended durations and seasonal balancing of power generation and consumption. However, planning for LTES usually necessitates the comprehensive coverage of its whole operation cycle, spanning from days to months, making the issue complex and intractable. To simplify the planning of a community integrated energy system (CIES) with LTES, this study proposes a time horizon compression (THC) method and formulates a concise long-term planning model for CIES with compressed time horizons. Then, robust optimization method with a budget uncertainty set is employed to develop a robust THC model, aimed at addressing data uncertainties in CIES planning. The proposed robust THC model is implemented in the planning of a CIES with high penetration of renewable energy sources, with the objective of minimizing the total annual cost. The results demonstrate that the proposed model can efficiently solve the complex CIES planning problem, resulting in a 42.77% acceleration in optimization speed. Additionally, the diversity and differentiation in THC configurations is investigated to enhance the implementation of THC in long-term CIES planning. The effectiveness of solution robustness and the significant effects of LTES on CIES are analyzed and validated in the case study. • A compact time horizon compression (THC) method is proposed for CIES planning. • Long-term energy storage is planned and investigated for seasonal energy balance. • Robust optimization is used for robust CIES designs with a budget uncertainty set. • The results indicate that THC reduces 42.77% solving time while keeping accuracy. • Applied conditions are analyzed and proposed for appropriate THC implementation. [ABSTRACT FROM AUTHOR]

Operational flexibility is to depict the ability of a power system in guaranteeing a secure and high-performance system operation, which is essential for distribution systems (DSs) with high renewable energy generation (REG) penetration. However, due to the dispersed integration of energy storage systems and uncertain REGs, the available regulation capability exhibits spatio-temporal coupling. It poses a challenge to quantify flexibility considering multiple locations and time periods. This paper proposes a novel analytical region-based method to characterize the flexibility considering spatio-temporal coupling, where the total dispatch budget and carbon emission constraints are considered to guarantee economic and environmental benefits. Leveraging the basic feasible solution theory, we derive an analytical formulation of the flexibility region, expressed as a set of linear inequalities. Note that no inner or outer approximation techniques are employed in the deduction procedure. The effectiveness and scalability of the proposed method are validated on test systems. Case studies demonstrate that the proposed method enables precise quantification and visualization of operational flexibility for DSs. The derived flexibility region is a closed polyhedron in a high-dimensional space, signifying the maximum ranges of uncertainties that can be accommodated. • A region-based method is developed for quantification and visualization of operational flexibility in distribution systems. • An analytical expression of the flexibility region under spatio-temporal coupling is derived from the dispatch model. • Total dispatch budget and carbon emission constraints are considered in flexibility quantification. [ABSTRACT FROM AUTHOR]

• Solved the optimal operation problem for IMS in market environment with uncertainty. • Established a hierarchical distributed framework for cloud-edge coordination. • Proposed a bi-level distributed optimization model with fair price mechanism. • Analytical target cascading and augment Lagrange method are integrated. • The diagonal quadratic approximation is employed for parallel solution. The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this paper. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establishing a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method. [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 • The adaptive droop method is used to design the primary control for each unit. • Only the local electrical parameters are used for droop control. • The coordination strategy is proposed for the multi group energy storage. • The typical mode transition scenarios are tested by the designed supervisory control. • The play and plug can be satisfied and accurate load power is realized. Abstract The hierarchical control strategy is proposed for DC microgrid with distributed generators, energy storage systems and loads. In order to maintain power balance and smooth bus voltage fluctuation during system operation due to dynamic load and resource variation, the designed hierarchical control is divided into two layers. In the primary layer, the novel adaptive droop controller with voltage feedback compensation is constructed for distributed generators and energy storage systems by taking advantage of local electrical parameters, which can realize the dynamic load power sharing and reduce bus voltage deviation. The distributed generators may operate between maximum power point tracking mode and droop control mode by bus regulation units when power supply is larger than the loads demand and the charging capacity of energy storage systems. The state of charge can also be adjusted for energy storage through the designed adaptive droop scheme. On the other hand, the secondary supervisory control is designed to reduce power exchange and improve the system stability. Using the designed hierarchical control, the DC microgrid can operate in four different modes according to which source responsible for the bus voltage stabilization. Simulation and comparison results validate that the proposed strategy may smooth the bus voltage fluctuation and realize the dynamic power sharing. [ABSTRACT FROM AUTHOR]

Through ad vanced information communication and management system, virtual power plant (VPP) can realize the aggregation and coordination optimization of distributed energy, energy storage system, controllable load and other distributed energy resources. However, when making real-time price decisions according to users' demand response (DR) characteristics, the optimization effect of VPP is still limited by the evaluation accuracy of users' DR potential and the computational burden of continuous decisions. By combining gate recurrent unit (GRU) and attention mechanism (AM), Neural Turing Machine (NTM) can extract users' response features in different environments and improve the accuracy of evaluating DR potential. Subsequently, based on the evaluation results, a deep deterministic policy gradient (DDPG) algorithm relying on prioritized experience replay (PER) is used to formulate a real-time electricity price plan. Ultimately, VPP achieves multi-objective optimization through DR management, which helps to increase the consumption amount of renewable energy resources, smooth its power fluctuation, and reduce users' electricity cost. Case study results show that the proposed method can improve the accuracy of the DR potential evaluation, reduce the response deviation to about 3%, and enhance the real-time decision calculation efficiency by 17%, which helps to optimize the smooth consumption of renewable energy. • Real-time pricing method for demand response (DR) management in VPP. • Considering the evaluation accuracy of users'DR potential with a NTM model. • Considering prioritized experience for making efficient price decision. • Presenting a multi-objective optimization model based on RE's consumption. [ABSTRACT FROM AUTHOR]

• A frequency regulation model for microgrid with share energy storage is established. • A DRL-based economic frequency regulation method is proposed. • Performance and operating cost of frequency regulation are considered together. • Multiple frequency regulation methods are compared and analyzed. • Results show that the proposed method has obvious advantages in integrated benefit. The microgrid is one of the fundamental ways to consume renewable energy, and the safety and economy of its frequency regulation are widely concerned and studied. For the microgrid with shared energy storage, a new frequency regulation method based on deep reinforcement learning (DRL) is proposed to cope with the uncertainty of source load, which considers both frequency performance and the operational economy of the microgrid. Firstly, a frequency regulation model for the microgrid is developed by sharing the frequency regulation potential of energy consumers. Secondly, a command allocation model for smart generation control (SGC) based on the integrated benefit is proposed, where frequency safety and economy are combined. Then, a DRL framework is designed, and the twin delayed deep deterministic policy gradient algorithm is used to implement the SGC of the microgrid in the continuous action space. Finally, the effectiveness of the proposed frequency regulation method is demonstrated by designing comparative simulations in the isolated multi-microgrid, and the cost weight and responsiveness of energy consumers are analyzed. [ABSTRACT FROM AUTHOR]

As an important means to realize the energetic complementarity and improve the efficiency of renewable resources, the stand-alone microgrid (SAMG) system gains attention increasingly, especially in islands and remote areas. In this paper, considering the interest conflict of the distribution company and the distributed generation owner, a new multi-objective optimal planning model is formulated for medium voltage SAMG. Besides, to avoid the power constraint of distributed generation (DG) once the over-limit voltage occurs, a novel two-step power dispatch control method including the voltage regulation strategy is proposed, in which the absorption of distributed power by energy storage system (ESS) and the reactive power adjustment though its power control system are used to regulate voltage. The goal of this paper is to search the Pareto-optimal front of the site and capacity of DG as well as the contract price between both parties, and thus can provide effective references for practical planning of SAMG. Considering the high cost of ESS, the investment analysis of ESS is also discussed in the paper. [ABSTRACT FROM AUTHOR]

Independent microgrids (MGs) consisting of diesel generator (DG), photovoltaic (PV), and energy storage system (ESS) are becoming a cost effective solution for the power supply in remote areas. However, besides PV intermittence, limited reserve and time-consuming replenishment of diesel fuel in remote areas make it challenging to guarantee long-period reliable power supply. In this paper, a risk-aware energy scheduling and stochastic optimization method is proposed to enhance long-period operational reliability of independent MGs. The possible extreme scenarios in the future are considered in an energy scheduling optimization model (ESOM). Based on energy forecast results of PV and loads for the next 7 days, ESOM maximizes the reliable power supply probability by optimizing energy scheduling strategies and reserve requirement for future operational risk simultaneously. Subsequently, a day-ahead stochastic optimization model is established to determine optimal power scheduling strategies of DG, PV, ESS, and flexible loads. The conditional value at risk (CVaR) is used to address the operation risk caused by uncertainties of PV and loads. Compared with traditional day-ahead optimization methods by numerous simulations, the proposed method has less expected load losses and PV curtailment, as well as less total supply-demand deviation. The resistance for future operational risks of independent MGs is therefore significantly enhanced. • A risk-aware dynamic optimization method is proposed for independent microgrids. • Future operational risks are considered in the energy scheduling model. • Uncertainties are considered in the CVaR-based day-ahead optimization model. • The long-period operational reliability is significantly enhanced. [ABSTRACT FROM AUTHOR]

The rapid development of digital economy has led to a dramatic increasement of internet data centers (IDCs), which consume a large amount of electricity. The diverse data-processing demands, high power consumption and workload uncertainty put forward a high requirement for the economical and secure operation of IDCs. As information technology (IT) devices are driven by direct current (DC), flexible substation (FS) has been gradually utilized to provide DC power for IDCs. This paper proposes robust operation strategies for minimizing IDC power consumption with FS integration. First, the linearized IDC power consumption model based on the technology of dynamic voltage and frequency scaling (DVFS) is proposed to describe the operating state of IT devices. Then, considering the FS-based coordinated operation of IDC, photovoltaic (PV) station and energy storage system (ESS), the deterministic power consumption minimization model of IDCs is established. Considering the workload uncertainty, the operation strategies based on distributionally robust optimization (DRO) for IDCs are further proposed. Finally, the effectiveness of proposed method is validated on a modified practical network with IDCs integrated. Results show that through the dispatch of workload and power flow regulation of FS, the power consumption and dropping workload of IDCs are effectively reduced. • The linearized power consumption model of data centers is proposed. • Variable resources are coordinated with flexible substation integration. • Distributionally robust optimization strategies are proposed for data centers. • The power consumption and dropping workload are effectively reduced. [ABSTRACT FROM AUTHOR]

• Typical configuration and four operation modes of DC Microgrid. • Power sharing control in hierarchical control for energy storage units. • Power balance control in hierarchical control for balance state of charge. • Virtual inertia control for power generation and Load Side Control for load consumption. • The autonomous operation of isolated DC microgrid can be realized without communication. In the DC microgrid, renewable energy sources are increasingly applied as distributed generations to replace the traditional energy at remote locations. Meanwhile, energy storage system needs to be employed to maintain the balance between fluctuated power generation and load consumption. In this paper, the coordinated control strategy is proposed for the stability operation of isolated DC microgrid. In order to avoid over-charging or deep-discharging for certain energy storage units, the novel hierarchical droop control scheme is introduced to balance state of charge among energy storage units to. Additionally, the virtual inertia control is designed to adjust the output power of renewable energy generation, and the fuzzy iterative method is added to reduce the voltage deviation induced by droop control. According to the bus voltage variation and the preset value of threshold, the load side control is constructed to guarantee the stability operation of high-priority loads. Finally, the autonomous operation of isolated DC microgrid can be realized without relying on communication. Comprehensive cases based on real-time digital simulator are provided to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]