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Hydropower can aid in compensating for wind and photovoltaic power output fluctuations and uncertainties. In this study, a multi-objective optimization model was established by integrating wind and photovoltaic power with hydropower scheduling considering the total power generation, power output stability, and influence of hydropower on a downstream riverine ecosystem. An improved adaptive reference point-based multi-objective evolutionary algorithm was employed to solve the wind–photovoltaic–hydropower system problem with various complicated constraints. Moreover, the large-scale system decomposition principle was used to decouple a wind–photovoltaic–hydropower system into a wind–photovoltaic compensated subsystem and a hydropower system. A combined solution method was developed according to the subsystem characteristics to improve the model efficiency. Considering that direct crossover and mutation of hydropower systems may not yield feasible solutions, dynamic feasible regions for crossover and mutation were constructed for multi-objective optimal scheduling. Furthermore, a stochastic multi-criteria decision making model that accounts for the uncertainty of criterion information was established, and the non-dominated solution obtained using the improved multi-objective evolutionary algorithm was employed for decision-making. The results showed that the total power generation, power output stability, and downstream riverine ecosystem have strong competitive relationships, and the improved adaptive reference point-based multi-objective evolutionary algorithm can produce superior quality Pareto optimal solutions with uniform distribution. Subsequently, the stochastic multi-criteria decision making model was used to rank the Pareto optimal solutions, where each solution can obtain several ranks with different probabilities, providing extensive information for use in decision-making.

Presenting the results of long-term financial projections, particularly those of common interest in retirement planning such as portfolio value and retirement income, can be difficult using traditional risk measures . Long-term projected distributions tend to be very wide and offer limited insight to practitioners into what kind of adjustments might be required along the way to avoid undesirable outcomes. Short-term projections, where available, lack information about final, long-term outcomes. As a complement to the pure long-term and short-term risk measures associated with these distributions, we propose a third measure, expectation risk, which reflects the short-term risk in long-term outcomes. This measure is derived from the short-term distribution of long-term expectations. In the following, we demonstrate how to calculate these distributions for simple cases of portfolio value and retirement income. Further, we show how expectation risk can be used to estimate the magnitude of short-term adjustments that may be required to obtain a desired long-term income.

Experimental and numerical analyses on the thermal and hydrodynamic performance of volumetric solar receivers were conducted. Silicon carbide (SiC) absorbers with various pore structures were systematically investigated on a laboratory-scale test platform. A three-dimensional theoretical model coupling the fluid flow with internal heat transfer was presented. The major characteristics of the thermal and hydrodynamic behavior combining radiation, thermal conduction, and interphase convection in the absorber were presented. In addition, the key design parameters were systematically analyzed. The experimental results revealed that a small pore diameter significantly enhances the volumetric heat-transfer coefficient, which in turn increases the fluid temperature and subsequently leads to a high thermal efficiency. Under high local thermal non-equilibrium state, the non-uniform hydrodynamic characteristic significantly influenced due to the increase in the fluid viscosity at the hot spot, especially in the case of a small-pore-sized absorber, thereby increasing the risk of overheating. For absorbers with similar porosities (ϕ ∼ 0.85), dp decreased from 5.83 mm to 2.22 mm, and the thermal efficiency η increased by nearly 3%. Moreover, the simulation results revealed that the absorber unit with dp of 2.22 mm and ϕ of 0.95 exhibits the maximum thermal efficiency of 72.48%.

For better time-allocation of stored energy, the solar greenhouse (SGH) is equipped with some storage devices designed economically for local weather: wall storage actively managed with energy-store/retrieve fans and Safety Energy (SE which is a solar collector and fully thermally isolated heat tank) designed for non-regular extreme weather. A proactive energy management process, addressing the optimal energy utilisation through dynamic cooperation of the wall and the SE, is presented in this paper. Based on probabilistic weather forecast and a SGH thermal model, found by system identification, the operation set-points are optimised proactively by minimising the plant probable thermal “cost” and weather-related risk in a scheduling period to take pre-emptory action against potential emergencies. The optimisation is formulated in a two-level control scheme. A master problem optimises the primary (wall-soil) storage operation against the expected weather, and a sub-problem operates the SE as a supplement to the limited wall storage in order to create a better indoor environment. The main task of the slave problem manager is to find the optimal SE operation under probable extreme weather to keep reserves to minimise any risk of severe crop loss. The overall optimisation is solved by a hybrid evolutionary algorithm based on a genetic algorithm. The tests show good potential for energy saving and crop cold stress minimisation, as well as great tolerance to forecast errors for most of the cases in Monte-Carlo simulation. The capacity of the proposed real-world system to implement the tested risk management scheme over web “recommendations” satisfies the need to close the loop of an effective Internet of Things (IoT) system, based on the MACQU (Management And Control for QUality) technological platform.

In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a multi-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIR) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIR model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a multi-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models.

With the increasing emphasis on environmental problems and climate change, renewable energy sources have been developed globally to push modern power systems towards sustainability. However, the weather-dependent and non-dispatchable features of renewable energy sources often hinder their integration into power grids and also pose a challenge for peak load regulation. Recently, the complementary operation of multi-energy hybrid systems has been attracting increasing attention as a promising way to overcome the mismatch between renewable energy supply and varying load demand. Multi-energy systems should be operated considering multiple uncertainties since a deterministic method only captures a fixed snapshot of a constantly changing system. In this study, the obtained short-term peak shaving operation of a hydro-wind-photovoltaic hybrid system is developed as a stochastic programming model. The uncertainties of renewable energy production and load demand are thoroughly simulated in the form of synthetic ensemble forecasts and scenario trees. To enhance the computational efficiency, a parallel particle swarm optimization algorithm is developed to solve the stochastic peak shaving model, in which a novel encoding scheme and parallel computing strategy are used. The proposed framework is applied to a hydro-wind-photovoltaic hybrid system of the East China Power Grid. The results of three numerical experiments indicate that the framework can achieve satisfactory peak shaving performance of the power system and enable decision makers to examine the robustness of operational decisions. In addition, it is acceptable for decision makers that joint complementary operation of the hybrid system greatly enhances the peak shaving capacity (with the performance metrics being improved by 95.7%, 96.4% and 30.5%) at the cost of 0.11% loss of total power generation.

This paper experimentally analyzes the thermal performance of a volumetric solar receiver with a series pore structures of silicon carbide foam. Twelve silicon carbide foam specimens with various porosities and pore diameters were systematically tested on a lab-scale test platform. Three-dimensional temperature distribution, including irradiated surface temperature distribution and internal solid temperature, were obtained. Non-uniform temperature distribution on the irradiated surface and internal field, as well as the corresponding thermal efficiency under different working conditions, was analyzed. The results demonstrate that pore diameter has a more significant effect on thermal performance than porosity, as the characteristics of the specific surface area among the examined samples were mainly determined by pore diameter. For the detected solid temperatures, the highest exceeded 1000 °C without any damage to the porous structure, and the maximum mean outlet air temperature exceeded 544 °C. The thermal efficiencies of the samples with smaller pore diameter were markedly higher in most cases. The best thermal efficiency was 85.4%, which was obtained from the specimen with pore diameter of 2.27 mm and porosity of 0.663. Optimal geometric properties of SiC foam absorbers may be characterized by small pore diameter and high porosity, which can beneficial by both the advantage of uniform irradiation absorption and effective interphase convection. The experimental data presented in this paper can provide a clear reference for the theoretical model and engineering design.

In this paper, a Monte Carlo ray-tracing method for modeling the incident irradiation propagation in a porous absorber with linear variable pore structure is presented. An acceptance-rejection method (ARM) is employed to generate each step size of the photon’s free path according to the specific radiative characteristics of the anisotropic porous medium. The method we proposed overcomes the limitation of the inverse transform method (ITM) by avoiding the integration process to obtain the cumulative distribution function. Using this method, the volumetric distribution in an absorber with a linear variable pore structure is determined. Three typical linear pore structure layouts—increasing (I-type), decreasing (D-type), and constant (C-type)—are analyzed. In general, the D-type layout achieves excellent optical efficiency and homogeneity of solar irradiation distribution. A sparse porous structure is beneficial for the in-depth propagation of photons, but it also increases the probability of photons scattering out of the medium. Therefore, increasing the density at the backside to intercept the ray effectively improves the optical efficiency. The model developed in this work is useful for understanding the propagation of solar irradiation distribution in a porous absorber with an anisotropic media, which is important for the thermal design of volumetric receivers.

MUC1 is a tumor-associated antigen (TAA) overexpressed in many tumor types, which makes it an attractive target for cancer immunotherapy. However, this marker is a non-mutated antigen without high immunogenicity. In this study, we designed several new altered peptides by replacing amino acids in their sequences, which were derived from a low-affinity MUC1 peptide, thus bypassing immune tolerance. Compared to the wild-type (WT) peptide, the altered MUC1 peptides (MUC11081-1089L2, MUC11156-1164L2, MUC11068-1076Y1) showed higher affinity to the HLA-A0201 molecule and stronger immunogenicity. Furthermore, these altered peptides resulted in the generation of more cytotoxic T lymphocytes (CTLs) that could cross-recognize gastric cancer cells expressing WT MUC1 peptides, in an HLA-A0201-restricted manner. In addition, M1.1 (MUC1950-958), a promising antitumor peptide that has been tested in multiple tumors, was not able to induce stronger antitumor responses. Collectively, our results demonstrated that altered peptides from MUC1, as potential HLA-A0201-restricted CTL epitopes, could serve as peptide vaccines or constitute components of peptide-loaded dendritic cell vaccines for gastric cancer treatment.

OX40 (CD134) is a tumor necrosis factor (TNF) receptor expressed mainly on activated T cells and transmits a potent costimulatory signal once engaged. Agonistic anti-OX40 monoclonal antibody (mAb) enhances tumor immune response leading to therapeutic effects in mouse tumor models. However, when tested in phase I clinical trials it did not show objective clinical activity in cancer patients. In this study, we examined the feasibility of nanoparticle (NP)-mediated delivery of anti-OX40 mAb to efficiently induce cytotoxic T lymphocyte (CTL) responses. The biodegradable poly(DL-lactide-co-glycolide) nanoparticle (PLGA-NP) carrying anti-OX40 mAb, anti-OX40-PLGA-NP, was prepared by double emulsion method and showed an average diameter of 86 nm with a loading efficiency of 25%. We found that anti-OX40-PLGA-NP induced CTL proliferation and tumor antigen-specific cytotoxicity as well as cytokine production more strongly than free anti-OX40 mAb. These results suggest that PLGA-based nanoparticle formulation may provide efficient delivery system of anti-OX40 mAb for cancer immunotherapy.