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This study delved into the creation of a composite phase-change material (PCM), named SBNSC, for potential application in cold storage air-conditioning systems (8–16°C). The composite PCM was formulated using sodium sulfate decahydrate (Na2SO4·10H2O) as the primary PCM, borax as a nucleating agent, ammonium chloride (NH4Cl) as a temperature control additive, sodium hexametaphosphate as a crystal modifier and carboxymethylcellulose as a thickening agent. The D-optimal mixture design was employed to optimize the composition, and this resulted in the identification of an optimal formulation characterized by the most desirable properties. The thermal properties were evaluated using differential scanning calorimetry and revealed a phase-change temperature of 15.40°C. X-ray diffraction analysis confirmed the crystalline nature of the composite PCM. After 100 cooling and heating cycles, the crystallization temperature was 9.50°C, the melting temperature was 15.50°C and the supercooling degree was 0.70°C. The enthalpy values showed an 11.22% decrease, indicating reliable thermal performance. The study concluded that the optimal formulation (SBNSC) exhibits commendable thermophysical properties, demonstrating its potential for effective application in phase-change cold storage air-conditioning systems.

Aggregation of electric vehicles (EVs) is a promising technique for providing secondary frequency regulation (SFR) in highly renewable energy-penetrated power systems. Equipped with energy storage devices, EV aggregation can provide reliable SFR. However, the main challenge is to guarantee reliable intra-interval SFR capacities and inter-interval delivery following the automatic generation control (AGC) signal. Furthermore, aggregated EV SFR provision will be further complicated by the EV charging time anxiety because SFR provision might extend EV’s charging time. This paper proposes a deliverable EV SFR provision with a charging-time-constrained control strategy. First, a charging-time-constrained EV aggregation is proposed to address the uncertainty of EV capacity based on the state-space model considering the charging-time restriction of EV owners. Second, a real-time economic dispatch and time domain simulation (RTED-TDS) cosimulation framework is proposed to verify financial results and the dynamic performance of the EV SFR provision. Last, the proposed charging time-constrained EV aggregation is validated on the IEEE 39-bus system. The results demonstrate that with charging time-constrained EV aggregation, the dynamic performance of the system can be improved with a marginal increase in total cost. More importantly, the charging time constraint can be respected in the proposed SFR provision of the EV aggregation.

Low-income communities have challenges obtaining equal access to electricity, so it is important to implement energy justice. Meanwhile, increasing installations of distributed generators (DGs) in distribution systems is a viable means to promote energy equity. Therefore, this work explores a new planning method to address the siting and sizing problem of DG units with an energy equity constraint embedded in the model, and provides concluding guidelines as a rule of thumb for future DG planning considering energy equity. In this paper, first, the DG siting and sizing problem is formulated as a stochastic bi-level model, where energy equity is considered as an energy burden constraint. The upper level determines the optimal sites and sizes of DGs under investment and energy burden constraints, while the lower level optimizes the distribution operation. Next, a solution method is proposed by applying the Karush-Kuhn-Tucker optimality conditions to convert the stochastic bi-level model to a single-level model. A decomposition approach and Progressive Hedging Algorithm are used to further simplify the single-level model into multiple easy-to-solve subproblems. Finally, numerical studies are performed on two test systems to verify the effectiveness of the proposed model. Technical rule-of-thumb guidelines are presented for siting and sizing DGs considering energy equity.

A new concept called virtual inertia scheduling (VIS) is proposed to efficiently handle the increasing penetration of inverter-based resources (IBRs) in power systems. VIS is an inertia management framework that targets security-constrained and economy-oriented inertia scheduling and generation dispatch with a large scale of renewable generations. Specifically, it determines the appropriate power setpoint and reserved capacity of synchronous generators and IBRs, as well as the control modes and control parameters of IBRs to provide secure and cost-effective inertia support. First, a uniform system model is employed to quantify the frequency dynamics of IBR-penetrated power systems after disturbances. Leveraging this model, the s-domain and time-domain analytical responses of IBRs with inertia support capability are derived. Then, VIS-based real-time economic dispatch (VIS-RTED) is formulated to minimize generation and reserve costs, with full consideration of dynamic frequency constraints and the derived inertia support reserve constraints. The virtual inertia and damping of IBRs are formulated as decision variables. A deep learning-assisted linearization approach is further employed to address the non-linearity of dynamic constraints. Finally, VIS-RTED is demonstrated on a two-machine system and a modified IEEE 39-bus system. A full-order time-domain simulation is performed to verify the scheduling results.

This paper proposes a decentralized and coordinated voltage and frequency (V-f) control framework for islanded microgrids, with full consideration of the limited capacity of distributed energy resources (DERs) and V-f dependent load. First, the concept of DER inadequacy is illustrated with the challenges it poses. Then, a decentralized and coordinated control framework is proposed to regulate the output of inverter-based generations and reallocate limited DER capacity for V-f control. The control framework is composed of a power regulator and a V-f regulator, which generates the supplementary signals for the primary controller. The power regulator regulates the output of grid-forming inverters according to the real-time capacity constraints of DERs, while the V-f regulator improves the V-f deviation by leveraging the load sensitivity to V-f. Next, the static feasibility and small signal stability of the proposed method are rigorously proven through mathematical formulation and eigenvalue analysis. Finally, a MATLAB-Simulink simulation demonstrates the functionalities of the control framework. A few goals are fulfilled within the decentralized and coordinated framework, such as making the best use of limited DERs’ capacity, enhancing the DC side stability of inverter-based generations, and reducing involuntary load shedding.

High penetration of converter-interfaced renewable energy resources will significantly change the swing dynamics between synchronous generators (SGs) in future power systems. This article examines the impact of high converter penetration on wave-like disturbance propagation arising from sudden generator and load losses in radial (1-D) and meshed (2-D) power systems. To keep the uniformity assumption as converters are introduced, the rating of each SG is decreased with a converter resource making up for the reduction. Numerical simulations demonstrate that as the penetration level of constant-power grid-following (GFL) converters increases, the speed of disturbance propagation increases due to the reduced system inertia. Naturally, converters with the capabilities to positively respond to disturbances would in turn reduce the propagation speed. Analytical studies based on continuum models are presented for the 2-D system with SGs and constant-power GFL converters in order to visualize the disturbance propagation and validate numerical simulations based on differential-algebraic equations. In addition, fast active power control of converters can slow down the electromechanical wave (EMW) propagation and even contain it. These concepts are illustrated on the idealized radial and meshed systems and a reduced model of the U.S. eastern interconnection.

Within the deregulation process of distribution systems, the distribution locational marginal price (DLMP) provides effective market signals for future unit investment. In that context, this paper proposes a two-stage stochastic bilevel programming (TS-SBP) model for investors to best allocate battery energy storage systems (BESSs). The first stage obtains the optimal siting and sizing of BESSs on a limited budget. The second stage, a bilevel BESS arbitrage model, maximizes the arbitrage revenue in the upper level and clears the distribution market in the lower level. Karush-Kuhn-Tucker (KKT) optimality conditions, strong duality theory, and the big-M method are utilized to transform the TS-SBP model into a tractable two-stage stochastic mixed-integer linear programming (TS-SMILP) model. A novel statistics-based scenario extraction algorithm is proposed to generate a series of typical operating scenarios. Then, scale reduction strategies for BESS candidate buses and inactive voltage constraints are proposed to reduce the scale of the TS-SMILP model. Finally, case studies on the IEEE 33-bus and 123-bus systems validate the effectiveness of the DLMP in incentivizing BESS planning and the efficiency of the two proposed scale reduction strategies.

Using data from the China Health and Nutrition Survey (CHNS), an index of health frailty is developed for individuals ages fifty-five and older. The evolution of the Frailty Index with age is first contrasted with self-reports of health. Then the temporal moments of the data are examined by considering the covariance structure of shocks that arrive at different ages with frailty in subsequent years. Then a formal model is estimated that incorporates both age-dependent auto-regressive and transitory components. The covariance structure of frailty is found to be convex in age. Larger shocks arrive as the sample ages but they decline rapidly. The evolutions of shocks beyond a specific age are modestly persistent (auto-regressive). The initial age-dependent transitory shocks become a larger portion of the total health risk as age increases.

Pinus koraiensisSieb. et Zucc. (Korean pine) is an evergreen coniferous tree species with significant therapeutic and nutritional value in northeastern China. To assess variation and select highly productive and nutritionally valuable materials, 25 cone, seed, and kernel nutritional components traits were measured and analyzed. Coefficients of variation for phenotypic traits ranged from 6.32 % to 161.51 %, and the coefficients of most traits were approximately 10 %. Most traits showed significant or extremely significant differences among clones or groups, especially for amino acid content. Cone numbers were under moderate genetic control, and their repeated ability ranged from 0.42 to 0.53. Most other traits were under high genetic control, and their repeated ability was higher than 0.70. Despite the weak correlation with seed traits, cone number had strong correlations with other characters. Four principal components were obtained with a cumulative variance contribution of 93.51 %. The selected elite material will provide a theoretical basis for selection and breeding of P. koraiensis.