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For the ac/dc hybrid energy system, a resilience-oriented defense strategy is developed for survivability enhancement when subject to severe energy disruptions. On account of heterogeneous components, an integration index of synthetic resilience (SR) is formulated to assess the resilience capability of hybrid energy systems in the event of sudden energy changes. The derived SR is systematically illustrated in this work, which not only reveals its convexity feature but also visualizes the inherent self-healing resources during the ac/dc energy interaction. Based on the proposed resilience index and interlinking bridge of bidirectional converter, an economic defense strategy concerning uncertainties from renewable energies and loads is developed and system resilience is consequently reinforced by dispatching distributed energies according to individual marginal cost. Without the global coordination and mode transition, the system resilience is significantly strengthened and assessed by the proposed index and defense strategy, which formulates an operational paradigm for the microgrid-level energy hybridization considering load variation and renewable energy penetration. To completely demonstrate the effectiveness of the proposed index and defense strategy for the hybrid energy system, the controller-hardware-in-the-loop experiment results have been provided from six aspects of steady-state operation, dc demand variation, ac demand variation, renewable energy fluctuations, dc energy source failure, and ac energy source failure. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in ensuring stable operation. However, the development of system-level energy storage is hindered due to the restrictions of economy, geography, and other factors. Transitions of traditional power systems into integrated energy distribution systems (IEDS) have provided new solutions to the problems mentioned above. Through intelligent control management methods, the utilization of multi-energy-type resources both on the supply and demand sides shows the potential for equivalent storage characteristics. Inspired by the aggregation principles, this paper aims at proposing a novel model named generalized multi-source energy storage (GMSES), including the modeling and cooperation of three kinds of available resources: conventional energy storage (CES), multi-energy flow resources (MFR), and demand response resources (DRR). Compared with the conventional means of storage, GMSES can be regarded as a more cost-effective and flexible participant in the proposed hierarchical energy scheduling framework that can realize system-level storage services in IEDS. On this basis, a multi-timescale energy scheduling strategy is proposed to reshape the regulation of IEDS operations and deal with the fluctuations caused by renewable energy and loads, where the general parameter serialization (GPS)-based control strategy is utilized to select and control the responsive loads in DRR. Furthermore, a hierarchical scheduling algorithm is developed to generate the optimal set-points of GMSES. Case studies are analyzed in an electricity-gas coupled IEDS. The simulation results show that the coupled co-optimization GMSES model is conducive to achieving the goal of self-management and economical operation, while the influence of the underlying IEDS on the upper energy system is reduced, as the tie-line power fluctuations are smoothed out. [ABSTRACT FROM AUTHOR]

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines. [ABSTRACT FROM AUTHOR]

In order to pursue the low-carbon development around the world, a large scale of renewable generation will be connected to the power systems. Take the Great Britain (GB) as an example, the GB power system has a large wind energy integration potential. The intermittency of wind generation will have great impact on the system frequency stability. Electric vehicles (EVs) have a crucial role in decarbonizing the transport sector. To increase the utilization of wind energy, EVs are suggested to provide frequency response service to the power system due to their quick power reaction characteristic. This paper proposes a general dynamic EV frequency control strategy considering the travelling behavior of the EV users. A droop control method is used to regulate the EV charging/discharging power according to the frequency signal. A Forced-Charge Boundary (FCB) and a Forced-Charge Area (FCA) are proposed to guarantee sufficient energy in the EV battery for user’s travel at the plug-out time. A dynamic Virtual Energy Storage System (VESS) is developed to evaluate the frequency response capacity of the EV clusters. In the case study, the model of the GB power system is used to investigate the frequency control effect of the control strategy. The simulation results show that the proposed strategy provides effective EV frequency response to the power system and thus is able to facilitate the integration of wind energy. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy generation brings about variability and randomness, which poses challenges to the power systems due to a potential shortage of flexibility resources. Inverter heat pumps (IHPs) can be utilized to address this issue by providing flexible ramping capacity (FRC). However, unlike conventional generation that offers a fixed FRC, the FRC of individual IHPs and their aggregation are influenced by their operational constraints and the indoor temperature thresholds set by buildings. These thresholds, in turn, are affected by uncertainties in ambient temperature and solar irradiation. For the individual IHP, an IHP-FRC assessment model is established. This individual IHP-FRC assessment model is based on the building thermal dynamic model and user comfort model, incorporating uncertainties in ambient temperature and solar irradiation through affine representations. For the IHP aggregation within a virtual power plant (VPP), a two-stage FRC assessment method is proposed to assess the FRC of an IHP-VPP in both the day-ahead and intra-day periods. In the day-ahead period, the VPP-FRC is calculated using the Minkowski sum method to ensure resource adequacy for meeting ramping demands. In the intra-day period, the VPP-FRC is rolling adjusted according to the operating status of each IHP and sent to the dispatch centre of the power system. The max-min fairness (MMF) ramping demand allocation strategy is employed to ensure fairness in allocating ramping demands to individual IHPs within the VPP. The simulation with 10,000 households in a winter heating scenario is conducted. The results indicate that the proposed method provides sufficient ramping capacities during the day-ahead period, with an upward FRC maximum of 6.29 MW/10 min and a downward FRC of −3.65 MW/10 min. The IHP-VPP effectively responds to ramping demands during the intra-day period without affecting the fairness, indoor comfort, and considering uncertainties in solar irradiation and ambient temperature. • A framework for assessing the flexible ramping capacity (FRC) of the inverter heat pump - virtual power plant (IHP-VPP) is designed. • A two-stage affine assessment method is developed to interact FRC of IHP-VPP with dispatch center. • The ramping demand is allocated to each IHP by the max-min fairness (MMF) allocation method for the subsequent assessment cycle. [ABSTRACT FROM AUTHOR]

• To propose a multi-dimensional correlation scenario generation method which considers the time sequence, auto-correlation, and cross-correlation of renewable energy and multi-energy loads. • To propose a risk assessment method of the energy pipeline based on the complex network theory. • To propose a multi-objective bi-level energy network stochastic expansion planning model considering the expansion cost and pipeline risk. Efficiently using multiple energy sources, including renewable energy, is a focus of applied energy research. A regional integrated energy system (RIES) involves coupling use of multiple energy sources, which can improve energy efficiency using multi-energy complementarity. However, increasingly renewable energies and multi-energy sources load access into an energy system increase the multiple uncertainties of RIES, which has considerable impact on both system planning and operation. This study proposes a multi-objective stochastic planning method that is based on the multi-dimensional correlation scenario set generation method for RIES' expansion planning. The scenario generation method considers the characteristics, time sequence, autocorrelation, and cross-correlation of renewable energies and multi-energy loads. The pipeline risk index for energy network expansion planning is defined considering the energy pipeline's importance. A multi-objective stochastic planning model based on chance constraints of the energy network is developed to minimize the investment cost and the energy pipeline risk. Finally, to confirm the effectiveness and the applicability of the proposed model and method, certain numerical cases at Yangzhong City, China, are simulated. Two RIES expansion planning scenarios are then compared. Moreover, the Pareto fronts of the optimized expansion planning schemes are demonstrated, providing reference for balancing the energy network planning scheme economy and the energy pipeline risk. [ABSTRACT FROM AUTHOR]

• Security assessment method for inertia and frequency stability is proposed. • The evaluation indexes of inertia estimation and inertia requirement are considered. • Inertia interval in a high proportion of renewable energy systems is constructed. • Minimum inertia requirement to maintain frequency stability is constructed. With the increase in large-scale renewable energy and DC transmission, synchronous generators in power grids are gradually being replaced, resulting in a continuous reduction in inertia. The resulting low-inertia system influenced the frequency stability of the power grid. On the one hand, knowledge of the inertia level of the system is relevant to evaluate the frequency stability of the power system. On the other hand, knowledge of the anti-interference ability of the system is also relevant. Based on the frequency response mechanism of a conventional synchronous system, this study extended the concept of inertia of a synchronous generator by introducing virtual inertia and proposed an inertia evaluation method for power grids with high-penetration electronic devices. Subsequently, a minimum inertia estimation method considering fast frequency response was proposed based on the virtual inertia technology of grid-connected inverters. Finally, the effectiveness of the proposed model was illustrated using a case study based on the South-East Australian system. [ABSTRACT FROM AUTHOR]

Highlights • Thermostatically controlled loads (TCLs) are used to regional power grid control. • TCLs are modeled as a simplified first-order equivalent thermal parameter model. • A novel TCLs control algorithm is proposed to maximize renewable energy usage. • The algorithm uses the state-queueing model and modified colored power algorithm. • The algorithm considers not only comfort levels but also fairness of customers. Abstract The active development and application of renewable energy, such as wind power and solar power, has become an inevitable tendency, which will promote the increase of the proportion of renewable energy generation accordingly in regional power grids. The intermission and randomness of renewable energy generation lead to enormous challenges in the operation and planning of power systems. One of the current methods to accommodate and reduce renewable energy fluctuation is conventional storage devices, e.g. battery energy storage devices, but this approach has the limitation of a high operating cost, which hinders the technology from popularizing. We present a novel load control algorithm employing the technology of residential thermostatically controlled appliances to maximize renewable energy usage in regional power grids. The thermal dynamics of the thermostatically controlled loads are modeled as a simplified first-order equivalent thermal parameter model. The new load control algorithm, based on the state-queueing model with modified colored power algorithm, is developed to manage activities of residential thermostatically controlled appliances, which allows customers to set different colors as the response levels of their appliances. Such an algorithm can ensure both the comfort level and the fairness of customers. Both a practical case and a real case are applied to demonstrate the effectiveness and practicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

DC microgrids (MGs) are providing a pathway toward a zero-carbon-based future. The intermittent renewable energy sources (RESs) and non-linear constant power loads (CPLs) thrive in DC MGs, craving for effective coordinative control solutions to ensure the stability of DC MGs. In this paper, a robust autonomous sliding mode control (SMC) scheme is proposed for achieving a globally stable and decentralized power sharing operation of multiple dispatchable units (DUs) in CPL-integrated DC MGs. Firstly, by using the high-order finite-time observer (HOFTO) technique, the disturbances, such as the power coupling and parameter uncertainties between different DU-interfaced converters, are self-eliminated within a finite time without any output current sensor and communication link. Secondly, a decentralized control scheme synthesizing the robust SMC and droop control algorithm is proposed to achieve proportional power sharing among paralleled DUs and precise DC bus voltage regulation. The proposed control guarantees the global system's large-signal stability by ensuring the local stability of an individual converter, offering a simple yet effective stable coordinative solution. Finally, simulation and experimental results verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

• A P2P trading strategy is proposed for the energy balance service provider (EBSP). • The EBSP market power and market elasticity are considered in optimized pricing. • The EBSP could enhance the energy resource allocation ability of the P2P market. • The EBSP could obtain profit without harming the overall benefit of the microgrid. The peer-to-peer (P2P) energy trading technology can effectively promote the mutual exchange and complementation of energy among diversified prosumers in the community microgrid. To further improve the energy resource allocation performance of the P2P energy trading, an energy balance service provider (EBSP) can be involved in the market. EBSP is an independent commercial entity configured with high-capacity energy storage systems (ESS) or auxiliary power sources, participating in the P2P market for profit. The energy shifting ability of EBSP can further improve the overall benefit of the microgrid. However, the scale effect endows the EBSP with comparatively strong market power, which may impose influence on the equilibrium of the P2P market. From the perspective of EBSP, the P2P market can be regarded as a market with elasticity. In the existing researches on optimized pricing and trading strategies of the P2P market, such a characteristic was not fully considered, which may have negative impacts on the economic profit of the EBSP and the overall benefit of the microgrid. Therefore, a P2P energy trading strategy for EBSP considering market elasticity is proposed in this paper. A market equilibrium model for the community microgrid with EBSP is first established, which reflects the elasticity of the market. Then, an optimized pricing model and trading strategy are established and solved to maximize the EBSP's profit. Numerical results show that the proposed method can improve the profit of EBSP, meanwhile reducing the energy dispatching cost of prosumers, achieving peak load shifting and renewable energy self-consumption of the community microgrid. [ABSTRACT FROM AUTHOR]