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Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Inert gas is often used in the deoxygenation of microbial electrolysis cells (MECs) to maintain growth and viability of anaerobes. However, the effects of the gas atmosphere on hydrogen production and microbial community of MECs are often neglected. Here, the performances and biofilm microbiomes of MECs pre-sparged with different gases were compared. MECs pre-sparged with argon gas (Ar) yielded more hydrogen (3.73 ± 0.13 mol-H2/mol-acetate) and a higher hydrogen production rate (2.99 ± 0.17 L-H2/L-reactor-day) than MECs pre-sparged with N2 (3.41 ± 0.13 mol-H2/mol-acetate and 2.27 ± 0.28 L-H2/L-reactor-day, respectively). Microbiome analysis indicated that the relative abundance of Geobacter increased from 59.25% to 77.79% when the gas atmosphere in MECs shifted from N2 to Ar. Hydrogen production may have been catalyzed by nitrogenase from Geobacter and photosynthetic bacteria in MECs pre-sparged with Ar. These findings suggested that the gas atmosphere substantially influences the microbiome of anode biofilms and Ar sparging is most effective for enhancing hydrogen production in MECs.

Abstract In this study, we develop indices for the overall technical efficiency (OTE) and energy-saving target ratio (ESTR) using data envelopment analysis (DEA) to calculate the relative efficiency and energy-saving potential of 30 provinces in China from 1997 to 2014. The results are as follows: (1) the OTE of China is 79.187%, indicating that there is 20.813% potential for improvement. The OTE exhibits decreasing efficiency values from the coastal areas to the inland areas and has clear geographical relationships. The average values of OTE in the east, midland and west are 0.932, 0.694 and 0.703. Theoretically, the total energy savings of CE, HE, ME and BE are 11080.60PJ, 5124.71PJ, 4729.24PJ and 6797.39PJ. (2) Regarding CE, HE, ME and BE, the provinces with the highest comprehensive ranks are Henan, Shanxi, Shaanxi, and Gansu, which simultaneously have the greatest energy-saving potentials and energy-saving targets. (3) The HE has the largest average ESTR of 38.357% and the values for BE, CE, and ME are 25.759%, 23.874%, and 22.143%, respectively. The CE category is the greatest in total energy savings (40.171%), which is followed by BE (24.150%), HE (18.384%), and ME (17.293%).

This data storage report explains the methodology for a series of experiments conducted as part of the Hydralab Joint Research Activity called RECIPE to investigate the response of physical modelling experiments to climate change drivers. Links are provided to the key datasets collected during the experiments. The experiments are designed to elucidate the influence of flood sequences on braided rivers with erodible banks under different vegetated conditions. The experiments had two specific objectives The experiments were carried out in order to address the following specific objectives: 1. To quantify the impacts of flood sequences on channel morphology with different hydrograph inputs, 2. To determine the role of vegetation growth/age on the impact of flood sequences

Abstract Transportation de-carbonization is a complex problem involving the economy, population, technology and environment. Implementing the pathway simulation based on systematic methods will help to optimize the transportation sustainability plan. This study’s key motivation is that earlier research ignored the relationship between driving factors and the transmission process. To quantitatively identify the path and process of emission reduction, a hybrid system dynamics STIRPAT-SD model is proposed to explore the transportation optimization’s de-carbonization ability. This study fully considers the composition of elements and subsystems based on the STIRPAT theoretical model and visually shows the system’s feedback relationship. Transportation structural and technical optimization scenarios are set to identify the threshold reduction paths. It is found these optimization strategies have significant de-carbonization effects. And transportation structure policy has the highest de-carbonization efficiency, the emission intensity decreased by 9.1% under the TSS2 scenario (Transportation structure scenario). This study proposes a novelty model combining dynamic simulating processes with a significantly theoretical model to improve simulation and factor composition accuracy. And the joint scenario setting identifies the most effective de-carbonization pathway and clarifies the threshold of all possible pathways. Research findings can effectively track, test, predict the achievement of policy goals, and provide policy optimization references for the sustainable development related to the transportation system in practice.

China is the largest fruit producer and consumer market in the world. Understanding the growing conditions responses to climate change is the key to predict future site suitability of main cultivation areas for certain deciduous fruit trees. In this study, we used dynamic and growing degree day models driven by downscaled daily temperatures from 22 global climate models to project the effects of climate change on growing conditions for deciduous fruit trees under two representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios over 2 future time periods (represented by central years 2050s and 2085s) in northern China. The results showed a general increase of available winter chill for all sites under RCP4.5 scenario, and the most dramatic increase in chill accumulation could reach up to 36.8% in northeast regions for RCP8.5. However, the forecasted chill will decrease by 6.4% in southeast stations under RCP8.5 by 2085s. Additionally, the increase rate of growing season heat showed spatially consistency, and the most pronounced increase was found in the RCP8.5 by 2085s. For the southwest station, median heat accumulation increased by 20.8% in the 2050s and 37.1% in the 2085s under RCP8.5. Similar increasing range could be found in the northeast station; the median growing season heat increased by 19.8% and 38.8% in the 2050s and 2085s under RCP8.5, respectively. Moreover, the date of last spring frost was expected to advance and the frequency of frost occurrences was projected to decline in the study area compared to the past. Overall, the present study improves understanding regarding site-specific characteristics of climatic suitability for deciduous fruit tree cultivation in main producing regions of northern China. The results could provide growers and decision-makers with theoretical evidence to take adaptive measure to ensure fruit production in future.

As a relatively mature technology, biomass molded fuel (BMF) is widely used in distributed and centralized heating in China and has received considerable government attention. Although many BFM incentive policies have been developed, decreased domestic traditional fuel prices in China have caused BMF to lose its economic viability and new policy recommendations are needed to stimulate this industry. The present study built a regionalized net present value (NPV) model based on real production process simulation to test the impacts of each policy factor. The calculations showed that BMF production costs vary remarkably between regions, with the cost of agricultural briquette fuel (ABF) ranging from 86 US dollar per metric ton (USD/t) to 110 (USD/t), while that of woody pellet fuel (WPF) varies from 122 USD/t to 154 USD/t. The largest part of BMF’s cost composition is feedstock, which accounts for up 50%–60% of the total; accordingly a feedstock subsidy is the most effective policy factor, but in consideration of policy implementation, it would be better to use a production subsidy. For ABF, the optimal product subsidy varies from 26 USD/t to 57 USD/t among different regions of China, while for WPF, the range is 36 USD/t to 75 USD/t. Based on the data, a regional BMF development strategy is also proposed in this study.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.