This addresses errors in [1]. The first author's affiliation information in the footnote is mistyped. The corrected sentence should read:

Despite the myriad of techniques for calculating flexibility envelopes in buildings and power grids, their analytical monetization has received practically zero attention. This obscure issue reaches even more unknown depths in the multi-period (MP) setting, where intertemporality hinders the design of a clear pricing framework. This letter builds on the author's previous work, which proposed a novel methodology for the one-to-one flexibility-to-cost mapping in grid-friendly smart sustainable buildings (GF-SSBs), and discusses alternatives for mapping MP flexibility envelope to costs in practice. Their comparison provides key insights into how different cost structures may affect end-user behavior and the grid's ability to procure flexibility. This letter hopes to kick-start the discussion around “fair” or intrinsic flexibility value, an issue that has largely been sidelined.

Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when tracking global MPP (GMPP) under partial shading conditions (PSC), leading to poor efficiency and significant power loss. Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. Additionally, a rank-based selection scheme is adopted to choose best half of population for subsequent global and local search modes. A modified global search method with fewer parameters is devised to rapidly identify approximated location of GMPP. Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. MPSO-HALS is implemented into low-cost microcontroller for real-time application. Extensive studies prove the proposed algorithm outperforms bat algorithm (BA), improved grey wolf optimizer (IGWO), conventional PSO and P&O, with convergence time shorter than 0.3 s and tracking accuracy above 99% under different complex PSCs.

Offshore wind farms connected to the grid via MMC-HVDC should have fault ride-through (FRT) ability. However, without high-speed communications, it is difficult to achieve coordinated control between wind power converters and the MMC-HVDC converters. To address this problem, a coordinated control method that is designed based on the injection of harmonics is proposed. During the fault, the offshore MMC converter detects that the DC voltage has exceeded the threshold value, and then injects sequence harmonics into the offshore AC lines, which enables the wind power converters to cooperatively limit the power injected into the grid. Therefore, the coordinated FRT of multiple converters can be realized without additional communication between wind power converters and the offshore MMC converters. A detailed model of MMC-HVDC connected offshore wind farms is constructed in a real time digital simulator (RTDS), and the proposed method is verified by hardware-in-loop dynamic simulation experiments. In addition to the traditional method of only limiting the output voltage of the offshore MMC, the proposed method also coordinately limits the output current of the wind farm to reduce the output power of the wind farm. Therefore, the proposed method allows the DC voltage to be quickly limited within the allowable range, and the system can achieve safe and reliable FRT without communication.

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for each individual microgrid and meanwhile ensure the voltage security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time corrective scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into a single-level mixed-integer second-order cone programming problem through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve the solving efficiency. Finally, numerical simulations on modified 33-bus and 123-bus test systems with multi-microgrids verify the effectiveness of the proposed framework.

Unexpected events have underscored the need for proactive preparedness in local energy systems, especially strategic distribution companies (DISCOs). The state-of-the-art methods to take proactive approaches in DISCOs are to use battery energy storage systems (BESS). However, there is a lack of established approaches that include the inter-dependencies of techno-socio economic features. To overcome this limitation, this paper constitutes a multi-stage resilience-promoting proactive strategy with linear decisions to enhance the strategic DISCOs preparedness level to deal with unscheduled islanding operation under uncertainties. In particular, this strategy provides operational solutions to survive DISCOs under stressful conditions by incorporating the economic operation of cloud energy storage (CES), which manages many BESS, and correlated demand response (DR) programs in resiliency issues. To this end, the lexicography algorithm is employed to prioritize different objectives based on the pre-avoidance, avoidance, survival, and post-disaster stages, which captures the BESS' energy level as well as socio-economic features of DISCO and CES. The effectiveness of the proposed strategy is demonstrated both analytically and empirically. At first, the features of the proposed strategy are examined in responding to islanding state using a set of numerical studies conducted on the IEEE 69-bus test system in GAMS software. After that, insightful analyses are performed via DIgSILENT PowerFactory to verify the feasibility of the proposed strategy.

Multiple hybrid energy storage systems (HESSs) consisting of batteries and super-capacitors (SCs) are widely used in DC microgrids to compensate for the power mismatch. According to their specific energy and power characteristics, batteries and SCs are used to compensate low-frequency and high-frequency power mismatches, respectively. This paper proposes a decentralized power allocation strategy for dynamically forming multiple HESSs aided with a novel power buffer. The power buffer is a device combing a capacitor and a bidirectional DC-DC converter, it is used as an interface between the batteries and DC bus, allowing easy Plug-and-Play of different energy storage units and effective and efficient power allocation. First, the power buffer and SCs split the power mismatch into a low-frequency and high-frequency part with a modified I-V droop control. Then the power buffer transfers the low-frequency mismatch to the batteries for compensation based on their respective state-of-charges (SoCs), while the high-frequency part is dealt by the SCs directly. This new scheme further allows elimination of the DC bus voltage deviations. Finally, the real-time hardware-in-loop (HIL) tests of three case studies confirm the effectiveness of the proposed control strategy.

The intermittence and randomness of wind speed often lead to the fluctuation of wind turbine output power especially when operating in high wind speed areas. In this paper, a holistic framework of error-based ADRC for wind turbine is proposed to achieve the output power regulation, by designing an extended state observer and parameter optimization to estimate and compensate the total disturbances. A stability theorem of closed-loop system with error-based ADRC is proposed by the Lyapunov function, and the feasible region of the controller parameters is deduced accordingly. To achieve the best performance of error-based ADRC, the optimal solution of the controller parameters is determined by the proposed Aquila Optimizer fused Golden Sine Algorithm. Finally, the superiority of proposed control strategy is verified by simulation tests on a 5MW OpenFAST wind turbine. Compared with the traditional control methods adopted in the industry, the error-based ADRC has excellent regulation rapidity and power stability for constant power control of wind turbine.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Photovoltaics are exposed to partial shading conditions (PSCs). Bypass diodes are installed across series-connected PV modules to avoid the hotspot phenomena, which causes several peaks on the power curve. While a new approach has been presented to distinguish between the uniform shading conditions (USC S ) and the PSC S to reduce unnecessary search space area, it leads to faster convergence speed (CS). This paper proposes an improved Rat Swarm Optimizer algorithm (IRSO), based on maximum power point tracking (MPPT), to increase the convergence speed towards the maximum power point. Furthermore, a new approach has been developed to improve speed response during load variation for any dc-dc converter. To make the algorithm more straightforward, one dynamic tuning parameter is used. The proposed method was tested experimentally by implementing a SEPIC converter and the sampling time was adjusted at 0.05 s. The proposed method was successfully implemented experimentally, with an average tracking time of less than 1 s and an efficiency of 99.89% for different irradiance values and load varying conditions. Moreover, the comparison between the proposed method and the metaheuristic algorithms in this domain is implemented and shows the effectiveness of the proposed method in terms of fast tracking, simple implementation and high efficiency.

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

The stochastic economic dispatch (SED) provides a promising solution for short-term cost-efficiency with high renewable penetration. However, the long-term efficiency of SED, i.e., promoting efficient investments in both flexibility resources and renewable generations, has rarely been studied. We theoretically reveal the financial incentives for efficient investments in SED from both perspectives of the short-term and long-term performances: 1) We demonstrate the rationality behind the short-term failure of cost recovery or revenue adequacy, which provides proper signals to corresponding dispatchable generators, renewable generators, or system operator for long-term reconstruction and installation. 2) Through theoretical analysis of the statistical properties, we prove that the long-term expected profits of renewable and dispatchable generators provide appropriate signals to guide their future investments. In conclusion, the SED can indeed motivate the investments of flexibility resources and renewables to match up better with each other in the network, benefiting the system efficiency and reliability in the long run. Simulations are conducted for validation.

In this paper, a novel optimization model is formulated to optimize the scheduling of battery swapping stations (BSS) operating electrified public bus transit fleets. The BSS consists of a number of battery modules that are combined together to create a MW-scale battery storage system. As such, the formulated optimization problem aims at minimizing the running costs of the BSS via i) exploiting the low electricity prices in the market (i.e., charging the battery modules at low prices), and ii) utilizing the BSS in the provision of grid ancillary services. The proposed model considers the operational requirements of the bus transit fleet to satisfy the energy needs of the battery electric buses (BEBs) and maintain their defined timetable. Also, the model integrates the power distribution network constraints such as bus voltages and line capacity limits to ensure its reliable operation. The impact of the BSS participation in the provision of ancillary grid services on the operation, degradation, and lifetime of the battery modules is investigated in this work by using a proposed saving cost index (SCI). It is demonstrated that the proposed model propels the economic viability of the BSS operation concept.

In this paper, a blockchain-enabled distributed market framework is proposed for the bi-level carbon and energy trading between coal mine integrated energy systems (CMIESs) and a virtual power plant (VPP) with network constraints. To maximize the profits of these two entities and describe their complicated interactions in the market, the bi-level trading problem is formulated as a Stackelberg game considering integrating the energy market and the “cap-and-trade” carbon market mechanism. Meanwhile, in the CMIES, energy recovery units and belt conveyors can be flexibly scheduled and the pumped hydroelectric storage in the VPP is scheduled for energy management. To tackle uncertainties from PV outputs, the joint trading, and the energy management is solved by the distributionally robust optimization (DRO) method. In addition, for participants' privacy, the alternating direction method of multipliers (ADMM) - based DRO algorithm is applied to solve the trading problem in a distributed framework. Further, the Proof-of-Authority (PoA) blockchain is deployed to develop a safe and anonymous market platform. Finally, case studies along with numerous comparison cases are conducted to verify the effectiveness of the proposed method. Simulation results indicate that the proposed method can effectively reduce the system operation cost and regional carbon emission, reduce the conservativeness and protect the privacy of each participant.

A coordinated voltage regulation method based on model predictive control (MPC) is proposed in this paper for utilizing wind farms (WF) as black-start (BS) source to start up a thermal generating unit. The reactive power regulation devices with different dynamic response characteristics including wind turbine generators (WTGs), energy storage system (ESS), and static var generator (SVG) are coordinated by the proposed MPC to handle disturbances caused by ancillary machine start during the BS process. The reactive power sharing between WTGs is optimized to maximize the dynamic reactive power reserve. The capabilities of ESS and SVG in providing sufficient dynamic reactive power against disturbances are also fully exploited, which helps accelerate voltage recovery for low voltage ride through to avoid the tripping incidents of WTGs. The impact of active power on bus voltage variation due to low X / R ratio is also considered. The reactive power and active power of WTGs and ESS are coordinately controlled for handling voltage disturbance without harming frequency control. A WF with 33 WTGs rated 1.5 MW each is used in case studies to demonstrate the enhanced disturbance handling capability of the proposed voltage regulation strategy during BS progress.

From both technical and economical perspectives, all-dc offshore wind farm (OWF) is a trend for future offshore wind energy applications. Furthermore, in an all-dc OWF, both the capacitors in dc collection grid and offshore wind turbines have the potential for onshore grid frequency response. In light of this, this paper presents a model predictive control scheme, which coordinates the operation of offshore dc collection grid capacitors (dc-grid capacitors) and offshore wind turbines for the onshore frequency support. With this scheme, offshore dc-grid capacitors provide fast inertia support right after the frequency event, while offshore wind turbines provide primary frequency response for a longer period. A key feature of the proposed scheme lies in the ability to suppress the secondary frequency drop issue, which is caused by sudden power reduction of offshore dc-grid capacitors during the frequency recovery phase. An analytical framework is derived to validate this ability. Finally, simulation results verify the effectiveness of the proposed scheme.

Due to the energy transition process, distribution systems will feature a high penetration of distributed renewable energy sources (RESs). The multiple distributed generation can provide emergency power supply to critical loads against blackouts caused by natural disasters and malicious attacks. However, the uncertainty of RESs, the control mode variation of RESs together with energy storage systems (ESSs), and the interaction among distribution system operator (DSO) and RESs add increasing difficulties to load restoration decisions. This paper applies the coordination control strategy to enable RESs to regulate the frequency and voltage during restoration process. Then, distributed energy resource management systems (DERMSs) and its aggregation characteristic are studied in load restoration. Finally, a two-step scenario-based stochastic optimization with the DSO-DERMS interaction framework is formulated to utilize RESs for resilient distribution systems. At last, the proposed critical load restoration optimization method is validated on a modified IEEE 37 and 123 node test feeder to verify the effectiveness.

Present-day inverter control mainly employs phase locked loop (PLL) based current-controlled strategies. With growing penetration of inverter-based resources in power systems, stability issues associated with inverter control have been reported in weak grid scenarios. A natural and fundamental question to answer is whether this instability is inherent to PLL based current-controlled strategies or it can be resolved with proper frequency and voltage control loop design. This paper addresses this question by analyzing the small signal stability of the current-controlled inverter in island and weak grid operation scenarios. Torque analysis, which is well known for synchronous machine stability analysis, is adapted to uncover the stability enhancement effect of properly designed and parameterized outer-loop controls. Different types of loads are considered and investigated in the study. Electromagnetic transient (EMT) simulations are conducted on a test network to validate the analytical results. The results from this study indicate that with appropriate outer-loop voltage and frequency control design, the PLL based current-controlled inverter can be small signal stable with high inverter penetration, even in a 100% inverter-based system.

The increasing penetration of electric vehicles (EVs) brings new flexibility in power grid operation. As EVs serve the main purpose of transportation, EV owners will charge their vehicles based on their driving patterns. Given the clustering charging behavior, uncontrolled charging can burden the distribution grid, increasing system operation costs. As a distributed energy resource (DER) with a highly mobile and distributed nature, aggregated EVs also have the capabilities to provide ancillary services to the power grid to alleviate voltage violations and/or regulate frequency. This paper proposes a new bilateral trading and auctioning mechanism between aggregators and EVs. In this environment, EVs and aggregators can trade energy and ancillary services in a decentralized manner. The proposed EV transactive energy methodology for auctioning and clearing is based on the alternating direction method of multipliers (ADMM) optimization and blockchain technology. Simulation results for the proposed EV TRADing of Energy and Services (EV-TRADES) environment are provided using the NYISO market price data. Numerical results demonstrate the capability to coordinate EV charging and incentivize EVs to provide ancillary services.

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.