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Abstract Few studies of policy mix concerning renewable portfolio standards (RPS) and emissions trading (ET) investigate a scenario in which the retailer is required to comply with quota obligations. This paper focuses on the impact of RPS, with an ET counterpart, on the electricity market when RPS are imposed on the state grid companies. The evolution game is employed to model transactions between multiple buyers and multiple sellers. The results show that ET enhances the price competitiveness of electricity from renewable energy source (RES-E) and reduces power generating companies' profit at the same time. On the other hand, when the RPS level goes up, the state grid companies gain a windfall created by the difference between the lower wholesale price of thermal power and the unchanging retail price. The windfall will be used to pay for the increasing cost of supporting RES-E. Thus, the thermal power generating companies are confronted with the double cost when introducing ET and RPS, but the state grid companies do not pay for the cost of supporting RES-E. This paper appeals to the Chinese government for the assessment of policy effects from a policy mix perspective. Highlights • The evolution game is employed to model transactions between multiple sellers and multiple buyers. • The quota obligation imposed on the state grid company is studied. • Emissions trading hurts the renewable power generating companies' profit and enhances the price competitiveness of RES-E. • The state grid companies do not pay for the cost of supporting RES-E. • The thermal power generating companies bear both the cost of emissions trading and the cost of supporting RES-E. [ABSTRACT FROM AUTHOR]

Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models. [ABSTRACT FROM AUTHOR]

Fossil fuels are the main energy source to satisfy the worldwide energy demands. However, the energy demands are increasing and the supply of fossil fuels is decreasing, thus many countries are looking for other fuel sources. Differing from the traditional fuels, hydrogen is considered as one of the most promising energy sources due to its intrinsic features such as clean, efficient, safe and sustainable. Developing novel technologies for hydrogen production from renewable sources (such as biomass) becomes a core area for the investigation of hydrogen industry. Within this work, different pathways for hydrogen production including steam reforming, electrolysis, and biomass gasification have been systematically compared in terms of yield and cost. This comparison is unique since the systematic evaluation was conducted from many aspects for all the hydrogen production pathways, especially those by involving the biomass gasification that still lack of available literatures. The assessment methods involved energy analysis, exergy analysis and economic analysis. It was concluded that steam reforming remains the cheapest method of hydrogen production at 1.748 $/kg, however, steam reforming is not an ideal process currently or for the future, gasification and electrolysis remains competitive with high yield but requires relatively high initial and annual expenditure. For biomass gasification, though its energy efficiency is lower than steam reforming, it has relatively higher mass yield, demonstrating the feasibility of this process for hydrogen production. Further for biomass gasification, the selection of correct feedstock is a key to maximize its yield, i.e. a yield of 82.47% is possible with corn stover fed gasification. [ABSTRACT FROM AUTHOR]

The optimal operation of electric hydrogen coupling systems has gradually become a key issue because of the considerable attention being paid to hydrogen energy. Therefore, this study investigates the multi-time scale operation optimization of a rural electricity hydrogen natural gas coupling system（EHHGS）considering equipment uncertainty and response characteristics. First, the framework of an EHHGS in rural areas is designed, and the equipment in the framework is modeled finely. Furthermore, the uncertainty and response characteristics of each link in the EHHGS are analyzed from the source, coupling, and load ends. Thereafter, the multi-time scale operation optimization model of the EHHGS is constructed based on the time resolution optimization model of operation optimization. Finally, a numerical example is analyzed to verify the effectiveness of the uncertainty model of each link and the multi-time scale optimization model. [ABSTRACT FROM AUTHOR]

• An electric-hydrogen-heat hybrid energy storage system is constructed, and a closed-loop energy path of electricity-hydrogen-electricity inside the MECS is formed. • A multi-objective optimization model is constructed with the objectives of carbon emission reductions, investment costs, operating costs, and the goodness of fit. • A complex experiment which is close to the real situation was designed. In the analysis process of the calculation example, three typical days in spring, summer, autumn, and winter are selected to simulate the change throughout the year. With the effectiveness of carbon emission reduction and the trend of clean energy utilization, installed photovoltaic (PV) capacity is increasing rapidly. The multi-energy coupling system (MECS), including hybrid energy storage, can effectively reduce the volatility of PV output and reduce its reliance on the grid. An MECS model is constructed, with objectives of carbon emission reduction, economics, and reliability. The NSGA-Ⅱ algorithm is used and the Pareto frontier of optimal solutions is obtained. The calculation example proved the validity of the model. The energy storage ecosystem composed of battery (BAT), hydrogen storage (HYS), and heat storage (HS), can effectively reduce the BAT capacity configuration. The integrated heat system can increase the energy efficiency by approximately 29%. The system revenue of the reliability optimal scheme (ROS) is 3,305 thousand yuan less than the multi-criteria optimal scheme (MOS); however, its reliability is better than that of MOS, as illustrated by the standard deviation reduction of grid interaction by 4.78%.The energy management strategy has been proven to be feasible because the system can find better reliability and economy from PV, energy storage planning, arbitrage between purchase and sale price differences, and electricity-hydrogen-heat conversion processes. [ABSTRACT FROM AUTHOR]

A stochastic chance-constrained planning method is applied to build a multi-objective optimization model for virtual power plant scheduling. Firstly, the implementation cost of demand response is calculated using the system income difference. Secondly, a wind power plant, photovoltaic power, an electric vehicle group and a conventional power plant are aggregated into a virtual power plant. A stochastic scheduling model is proposed for the virtual power plant, considering uncertainties under three objective functions. Thirdly, a three-stage hybrid intelligent solution algorithm is proposed, featuring the particle swarm optimization algorithm, the entropy weight method and the fuzzy satisfaction theory. Finally, the Yunnan distributed power demonstration project in China is utilized for example analysis. Simulation results demonstrate that when considering uncertainties, the system will reduce the grid connection of the wind power plant and photovoltaic power to decrease the power shortage punishment cost. The average reduction of the system power shortage punishment cost and the operation revenue of virtual power plant are 61.5% and 1.76%, respectively, while the average increase of the system abandoned energy cost is 40.4%. The output of the virtual power plant exhibits a reverse distribution with the confidence degree of the uncertainty variable. The proposed algorithm rapidly calculates a global optimal set. The electric vehicle group could provide spinning reserve to ensure stability of the output of the virtual power plant. Demand response could optimize customers’ power consumption behaviors and improve the grid connection space of the virtual power plant. The electric vehicle group and demand response could achieve a linkage optimization effect between the generation side and demand side, achieving optimal system scheduling objectives. [ABSTRACT FROM AUTHOR]

In order to better understand the mechanism of NOx and N2O precursors (NH3 and HCN) from aspartic acid (Asp) pyrolysis, decomposition reaction networks resulting in the generation of NH3 and HCN were investigated by employing density function theory methods. After several pathways were analyzed in detail, two series of pyrolytic reactions containing three possible pathways were proposed. All the reactants, transition states, intermediates and products were optimized, also the electronic properties on these crucial points were discussed, which shows that Cα acts as the most active site to initiate the pyrolysis reaction, where the direct Cα-Cβ bond breakage, due to the atomic charge population of repulsion, led to one key route for the generation of HCN, and the transfer of Hα from Cα to Cβ resulting in another key route for the generation of HCN, while the transfer of Hα from Cα to N atom of Asp resulting in the key route for the generation of HN3. Further, the kinetic analysis based on speed control method in each key reaction pathway was conducted to further compare the generation of HCN and NH3 under various temperatures. The above results are in accordance with the related experimental results. [ABSTRACT FROM AUTHOR]

• Design a novel structure of virtual power plant incorporated by gas-power plant carbon capture, power-to-gas and electric vehicle aggregator (C2P-VPP) • Propose a tri-dimensional coordinated optimal dispatching model for C2P-VPP based on the impossibility theory of energy triangle. • Construct an improved fuzzy equilibrium coordination-based-model solution algorithm for C2P-VPP considering multi-party completely rational game. • Select the CIGRE medium-voltage power distribution system as the simulation system for verifying the effectiveness and applicability of the proposed. • Discuss the optimal dispatching strategy of the C2P-VPP under different operation scenario. This study proposes a novel structure of carbon-to-power-based virtual power plant (C2P-VPP) considering the flexible demand response and electric vehicle-to-grid aggregators (EVA). C2P is integrated by gas-power plant carbon capture (GPPCC), carbon storage devices (CS), and power-to-gas (P2G). Then, to balance the multiple objectives of the dispatching cost, the carbon emission and the output fluctuation, a tri-dimensional coordinated optimal dispatching model is construct from the perspective of the energy impossibility triangle problem. And the robust optimization theory is applied to characterize the uncertainty of wind power plant (WPP) and photovoltaic power generation (PV). Thirdly, to solve the above multi-objective model, an improved fuzzy equilibrium coordination-based-model solution algorithm is proposed. The algorithm integrates the fuzzy satisfaction theory, the multi-objective input income table, the entropy weight method into the complete information static game model. Finally, the CIGRE medium voltage distribution system is chosen for case study, the results show: (1) GPPCC could capture and store CO 2 in carbon storage devices, and P2G could convert the CO 2 into CH 4 for conventional gas turbines (CGT) providing flexible power output. Compared with the scenario only with CS or P2G, when they are both introduced, the carbon emissions reduced by 7.75 % and 3.82 %, the dispatching cost reduced by 2.90 % and 7.77 %, and the output fluctuation reduced by 10.03 % and 2.80 %. (2) When robust coefficient Г is from 0.85 to 0.95, the decision-maker is the risk preference type and willing to bear risk to pursue excess benefit. (3) Sensitivity analysis shows when the rated capacity ratio of CGT, GPPCC and P2G is from 8:4:1 to 2:2:1, the dispatching scheme are optimal. When the capacity ratio of WPP, PV and EVA is from 20:1 to 10:1, the objective value changes significantly, and the optimal equilibrium scheme could be established when the capacity ratio is higher than 10:1. Therefore, the proposed optimal dispatching model could provide an effective tools for decision makers to achieve the optimal dispatching schemes. [ABSTRACT FROM AUTHOR]

• Design a novel structure of virtual power plant connected with gas-power plant carbon capture, power-to-gas and waste incineration power (GPW-VPP). • Propose a nearly-zero carbon optimal operation model for GPW-VPP based on the information gap decision theory and fuzzy satisfaction theory. • Construct a Nash negotiation-based benefit allocation strategy for GPW-VPP considering the contribution factors of risks, benefits and carbon emissions. • Select the Lankao Rural Energy Revolution Pilot as the simulation system for verifying the effectiveness and applicability of the proposed. • Discuss the optimal operation strategy of the GPW-VPP under the extremely worst scenario. Aiming at utilizing a large number of distributed energy sources in rural areas such as straw and garbage biomass, rooftop photovoltaics, and decentralized wind power, this study designed a novel structure of a virtual power plant connected with gas-power plant carbon capture (GPPCC), power-to-gas (P2G), and waste incineration power (WI), namely, a GPW-VPP. Then, the information gap decision theory (IGDT) and fuzzy satisfaction method were applied to construct a nearly-zero carbon optimal operation model. In this model, the maximum revenue and minimum carbon emissions were selected as the initial objectives, which were converted into one maximum satisfaction objective. Three uncertainty variables, namely, wind power, photovoltaic power, and user's load, were described using the IGDT. Secondly, to optimize the distribution of the cooperative operation revenue for the entities in GPW-VPP, a Nash negotiation-based benefit allocation strategy is established considering the multidimensional contribution factors of risks, benefits, and carbon emissions. Finally, the Lankao Rural Energy Revolution Pilot program in China was selected as the case study, the results showed: (1) GPW-VPP can aggregate and utilize different types of distributed energy sources such as rural wind power plants (WPPs) and photovoltaic power generation (PVs) to realize the electricity–carbon–electricity cycle effect. (2) The proposed operation optimization model can measure the uncertainty risk and formulate an optimal plan considering the above dual objectives. When the deviation coefficient of the predicted objectives is 0.5, the uncertainty degree is 0.142, and the cost of the decision plan is less than the expected cost of decision maker. Compared with the maximum revenue objective, the operation revenue and carbon emissions reduced by 4.6% and 35.76% under the comprehensive optimization objective, respectively, (3) The proposed benefit distribution strategy can be used to formulate a better benefit distribution plan that meets the comprehensive contributions of multiple entities. Affected by the risk of output uncertainty, the benefit proportion of WPP and PV increased, but it was 1.64% lower than that in the traditional distribution plan. Affected by carbon emissions, the benefit proportion of biomass power generation decreased, but it was 0.57% higher than that in the traditional distribution plan. Overall, the proposed operation optimization model and benefit distribution strategy can balance the interest requirements of different entities and promote the optimal aggregation and utilization of rural distributed energy resources, which is conducive to the realization of a clean and low-carbon transformation of the overall energy structure. [ABSTRACT FROM AUTHOR]

• A mixed-integer nonlinear programming optimal siting and sizing energy stations and networks model is proposed. • The multi-objective synergy planning minimizes total annual costs and total electricity abandonment rate. • NSGA-II and TOPSIS can be used to solve the Pareto solution set and best compromise solution. • Three scenarios are carried out for synergy planning in terms of energy interaction and equipment selection. • Potential benefits of the proposed method are visualized, discussed and analyzed. Regional integrated energy system (RIES) contains multiple energy coupling equipment and differential energy demand, which demonstrates that establishing a complete source-grid-load-storage energy supply chain is conducive to renewable energy consumption, and improves RIES economy and energy utilization efficiency. This paper proposes a synergy planning approach for RIES energy stations (ESs) and pipeline networks (PNs) with energy interaction and equipment selection. First, this paper analyzes a RIES structure involving equipment selection and energy interaction, and describes the problem to be addressed in this paper. Secondly, ten equipment output models are built based on energy coupling methods, and a RIES synergy planning model is further developed to minimize the annual total costs (ATCs) and the total electricity abandonment rate (TEAR) with various constraints. Subsequently, Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are applied to solve the Pareto solution set and best compromise solution (BCS) of this mixed-integer nonlinear model (MINLP), respectively. Finally, optimal planning combinations are selected in three scenarios so as to verify the feasibility and validity of the proposed models and method. Innovations as well as further extensions of the proposed methods, are also discussed. Optimal equipment selection and output strategy help reduce 52.42% ATCs and 19.81% TEAR. Moreover, with considerations for energy interactions, RIES synergy planning achieves higher excess electricity consumption efficiency and further 1.40 M$ savings. [ABSTRACT FROM AUTHOR]

• The MLM and DAM is coupled and connected by electricity decomposition. • The W-T alliance is conducive to peak load regulation of the system. • The joint participation of W-T alliance in the market can reduce wind curtailment. • The joint participation of W-T alliance in the market increases economic benefits. In 2015, "Several Opinions on Further Deepening the Reform of the Power System" was issued. The new round of power system reforms proposes to give full play to the vitality of the power generation-side bidding market, which will help to establish a fair, reasonable, and active power market. Due to factors such as a lack of peak shaving capacity and imperfect market mechanisms, China's renewable energy participation in the power market faces many problems. To solve these, this paper proposes a two-level optimization model for a wind power plant and thermal power unit to participate in the medium and long-term electricity market, and day-ahead market transactions. First, the paper proposes the electricity quantity and electricity price determination method of annual bilateral negotiated transactions, monthly centralized bidding transactions and listings, and delisting transactions, and briefly describes how to decompose contract electricity into the day-ahead market in the medium and long-term market. Then, two ways for the wind power plant and thermal power unit to participate in the power market are proposed. With maximization of the market benefits as the objective function, and through comprehensive consideration of the system reserve, wind curtailment penalty, green certificate trading, and other issues, the two models of independent participation and joint participation in market transactions were established to study the problem of maximum profit on the power generation side. Finally, the analysis results of the calculation example show that the joint participation of the wind power plant and thermal power unit in the power market has additional benefits compared to independent participation. The wind power plant can complete the assessment index of the renewable energy quota for the thermal power unit, which does not need to purchase green certificates to complete the assessment indicators. The thermal power unit can provide reserve services for the wind power plant, avoiding output whenever necessary, reducing the cost of the curtailment penalty, and overcoming the threat of wind power plant output fluctuations to the system. The two alliances have greater profit margins. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality, promoting clean and renewable energy consumption and reducing CO 2 emissions have become important measures. Therefore, virtual power plants (VPPs) containing carbon capture devices have become a research focus. This study first builds a VPP model containing carbon capture devices, analyzes the cooperative operation mode of a carbon capture system and power-to-gas, and develops a comprehensive demand response mechanism for the VPP. Second, owing to the uncertainty of the electricity market clearing price and the natural gas market price, a risk dependence model between electricity price and gas price based on the copula–conditional value-at-risk theory is constructed. Then, a VPP three-level market optimization model considering risk dependence and carbon trading mechanisms is proposed, and an improved collaborative evolutionary algorithm that combines the NSGAII and AGE-MOEA algorithms is used to solve multi-objective problems. Finally, an example of a VPP is used to verify the effectiveness of the model. • The study constructs a VPP model framework where a CCS, CSS, and P2G work together. • It comprehensively considers the HDR and VDR of the VPP. • Apply copula and CVaR theories between electricity price and natural gas price. [ABSTRACT FROM AUTHOR]

• A synergy planning methodology of energy stations and networks is proposed. • The cost-based synergy planning model consists of power losses, siting and sizing, multiple constraints. • The model uses a hybrid approach including Genetic Algorithm, Dijkstra Algorithm and Simulated Annealing. • Four scenarios analyze the potential benefits of the methodology. • The proposed hybrid approach is an efficient tool for siting and sizing energy stations and networks. Siting and sizing of energy stations (ESs) and pipeline networks (PNs) significantly influence views on the economy of regional integrated energy system (RIES). In this paper, we present a synergy planning method for siting and sizing ESs and PNs. This study describes the basic structure of ESs and PNs in RIES, clearly points out influencing factors that should be considered in the planning model. Then, with flow velocity, pipe diameter, equipment capacity, and the location of ESs and PNs as variables, the cost-based function model is built based on many constraints. The Genetic Algorithm (GA) based heuristic algorithm and the PNs planning algorithm based on "Dijkstra Algorithm (DA) + Simulated Annealing (SA)" are used to solve the problem proposed in this paper. In the results and discussion, the proposed model and method are verified by a hypothetical region from OpenStreetMap. The economic changes are studied in a basic scenario and three comparison scenarios. The results show that synergy planning can save 293,320 $ and reduce the load peak-valley difference rate by 3.24%. Moreover, optimizing the flow velocity can reduce 0–32.04% lowest annual cost (LAC). [ABSTRACT FROM AUTHOR]

• Proposed a novel two-stage demand response for electricity retailers with energy storage system. • Designed a flexible operation mode for electricity retailers with energy storage system. • Construct a bi-level purchase and sale transaction coordinated model for electricity retailers. • Calculated the Karush-Kuhn-Tucker (KKT) conditions of the bi-level optimization model. • Discuss the influence of the two-stage demand response on the optimal transaction strategy. A new two-stage demand response is designed for the electricity retailers with energy storage system (ESS-ER) in the deregulated power market. The ESS-ER could response to the output of different power sources by adjusting the charging-discharging behavior according to the bidding power price. The paper models the two-stage demand response for electric power retailers and proposed a two-layer coordinated optimal model for the purchase and sale of the electric power retailers. In the upper layer model, the conditional value at risk method and robust stochastic theory are applied to describe the uncertainty influence of wind power and Photovoltaic (PV) power, and the minimum whole cost of power purchasing is taken as the objective. In the lower-layer, the power consumption behaviors of different customers are considered to get the maximum revenue of power selling by implementing differentiated demand response. Then, to solve the two-layer mathematical model, the lower-layer model is converted into the Karush-Kuhn-Tucker (KKT) optimality conditions. The results show that: (1) The two-stage demand response could smooth the curves of power purchasing and terminal users' load, which could bring more flexible transaction space. (2) The proposed two-layer transaction model could balance the cost and risk of power purchasing, bringing more trading opportunities for wind power and PV, which can also reduce the energy consumption cost of the end-users. (3) By introducing the risk cost coefficient, confidence degree and robust coefficient, the decision-makers can adjust the power trading behaviors, and establish the optimal power trading scheme in line with their expected situation. (4) When higher energy storage capacity is set, the efficiency of demand response rises. When the capacity ratio of wind to energy storage is 4:1, the efficiency of demand response reaches the best. When larger energy storage capacity is set, the demand response turns to be more effective. However, when the capacity ratio of wind and PV to energy storage is 4:1, the effect of demand response reaches the best. Overall, the proposed model could provide an effective tool for power retailers in China's electric power market. [ABSTRACT FROM AUTHOR]

Oat contains a large amount of polysaccharides and peptides, among which oat peptides have the function of reducing blood sugar levels in the body. This paper reviewed the peptides obtained from oat extraction and identification from literature and constructed the corresponding oat peptide database. Based on the DPP4 protein, a virtual screening of the peptide database was performed. One hundred nanosecond molecular dynamics simulations were performed for the six peptides obtained from the screening. The interaction information between different peptide molecules and DPP4 was analyzed from the stable binding conformations after the simulation, and the binding free energy between different peptide molecules and DPP4 was calculated. The results show that the peptide molecules obtained from the virtual screening can all stably bind to the DPP4 protein, among which two peptide molecules have relatively strong affinity with DPP4 and can be used as lead molecules for the subsequent design and modification of DPP4 inhibitors. The simulation results are informative for a deeper understanding of the structural characteristics of DPP4 and the molecular recognition mechanism between DPP4 and oat peptides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)