Consumption Rate Of Renewable Energy Research Articles

An optimal scheduling strategy for electricity-thermal synergy and complementarity among multi-microgrid based on cooperative games

Multi-microgrid (MMG) systems provide an effective way to convert renewable energy into other forms of energy for low-carbon utilization. However, the coupling of multi-energy and renewable energy brings a challenge to the stable operation and dispatch of the system. Therefore, an electricity-thermal-gas-hydrogen MMG complementary optimization model based on peer-to-peer (P2P) electricity-thermal synergy is proposed. The model focuses on the coupling and conversion of electricity, hydrogen, and thermal through a hydrogen energy storage system (HESS) combined with renewable energy, and incorporates integrated demand response (IDR) and ladder-type carbon trading mechanism (LCTM). Subsequently, based on the cooperative game and the alternating direction multiplier method (ADMM), a multi-energy synergistic and complementary optimal scheduling strategy is proposed, which increases the flexibility of multi-energy sharing. In addition, a revenue allocation optimization strategy that takes into account the fairness and renewable energy consumption rate is proposed to achieve the stability of the alliance system operation. Finally, the simulation results show the effectiveness of the proposed model and strategy, realizing the complementary sharing of regional multi-energy, enabling the expansion of the boundaries of optimal energy allocation, further improving the low-carbon economic operation capability of MMG, and reducing the dependence of the system on the energy market.

Multi-objective stochastic-robust based selection-allocation-operation cooperative optimization of rural integrated energy systems considering supply-demand multiple uncertainties

The carbon neutrality goal places greater demands on integrated energy system (IES) in rural areas. In line with this trend, a multi-objective stochastic-robust based selection-allocation-operation cooperative optimization model is established for planning of rural IES in this paper. Firstly, this paper construct three typical IESs at three levels (user, township and user-township interaction) and make economic selection. Secondly, in order to coordinate the objectives of technology-economy-environment and solve the multiple uncertainties existing in the system, a multi-objective stochastic-robust planning model is proposed. In the model, the objective function introduces indicators such as total cost, primary energy resource efficiency and renewable energy consumption rate. Then constructing stochastic hierarchical scenarios to characterize the stochastic uncertainty of parameters in the time dimension. And introducing typical scenarios into the box sets which characterize the supply-demand sides uncertainty. Finally, a sensitivity analysis is performed to discuss the influence of robustness parameters and biogas prices. The annual total cost of the user-township interaction system is 53.8 % and 8.4 % lower than that of the other two systems, respectively. And from the allocation results of selected system, primary energy resource efficiency is 0.57, total cost is 1.81 × 106 USD and renewable energy consumption rate is 99.46 %. In addition, from the operation results, it is analyzed that the output of the equipment is perfectly balanced with the user's load. Finally, it is obtained by sensitivity analysis that uncertainty budget and biogas price have an impact on the selection of discrete equipment, the capacity of continuous equipment and the total cost. And the optimal capacity of the system under different situations is given.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

Optimization configuration strategy for regional energy systems based on multiple uncertainties and demand response

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems.

Global Optimization and Quantitative Assessment of Large-Scale Renewables-Based Hydrogen System Considering Various Transportation Modes and Multi-Field Hydrogen Loads

In the past, hydrogen was mostly produced from fossil fuels, causing a certain degree of energy and environmental problems. With the development of low-carbon energy systems, renewable energy hydrogen production technology has developed rapidly and become one of the focuses of research in recent years. However, the existing work is still limited by small-scale hydrogen production systems, and there is a lack of comprehensive research on the whole production-storage-transportation-utilization hydrogen system (PSTUH2S), especially on the modeling of different hydrogen transportation modes and various hydrogen loads in different fields. To make up for these deficiencies, the specific physical and mathematical models of the PSTUH2S are firstly described in this paper, with a full account of large-scale water-electrolytic hydrogen production from renewable power curtailment and grid power, various hydrogen storage and transportation modes, and multi-field hydrogen consumption paths. Furthermore, to achieve the maximum economic, energy, and environmental benefits from the PSTUH2S, a multi-objective nonlinear optimization model is also presented herein and then solved by the hybrid method combining the nonlinear processing method, the CPLEX solver and the piecewise time series production simulation method. Lastly, case studies are conducted against the background of a region in northwest China, where hydrogen consumption capacity in various years is accurately assessed and the potential advantages of the PSTUH2S are demonstrated. As the simulation results show, the power curtailment of renewable energy generation can be reduced by 3.61/11.87/14.72 billion kW·h in 2025/2030/2035, respectively, thus contributing to a 4.98%~10.09% increase in the renewable energy consumption rate and millions of tons of carbon emission reduction in these years. In terms of the total equivalent economic benefits, the proposed method is able to bring about a cost saving of USD 190.44 million, USD 634.66 million, and USD 865.87 million for 2025, 2030, and 2035, respectively.

Optimization and scheduling scheme of park-integrated energy system based on multi-objective Beluga Whale Algorithm

To improve the consumption rate of renewable energy and ensure the stability of power and heat supply within the industrial park, a park-integrated energy system (PIES), which incorporates electric heating equipment, energy storage devices, and multiple micro sources, is established. Based on the established PIES, a price-based demand response (PDR) mechanism is introduced to establish a multi-objective optimization and scheduling model with two time scales: day-ahead scheduling and intra-day scheduling, aiming to minimize the impact on the main grid and ensure the flexibility of system operation and real-time scheduling. With the objectives of minimizing operating costs and maximizing environmental benefits for PIES, a hybrid algorithm named Non-Dominated Sorting Beluga Whale Optimization (NSBWO), which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Beluga Whale Optimization (BWO) algorithm is proposed to solve the optimization problem of the model. Simultaneously, an adaptive penalty function is introduced in the algorithm to handle constraint problems in optimization and additional penalty term for wind and solar curtailment is included within the constraints to improve the consumption rate of sustainable energy. The proposed NSBWO algorithm exhibits more robust search capabilities and faster convergence speed than the NSGA-II and MOPSO algorithm. Simulation results show that the proposed NSBWO algorithm can achieve low-carbon economic scheduling of the system in both long-term and short-term time scales and improve renewable energy consumption. Due to its flexibility, intra-day scheduling has lower operating costs than day-ahead scheduling.

Deep reinforcement learning-based optimal scheduling of integrated energy systems for electricity, heat, and hydrogen storage

The increasing load demands and the extensive usage of renewable energy in integrated energy systems pose a challenge to the most efficient scheduling of integrated energy systems (IES) because of the unpredictability and volatility of both the load side and renewable energy.Integrating heat storage and hydrogen storage technologies into integrated energy systems can effectively alleviate the phenomena of wind and solar power desertion caused by the instability of renewable energy sources. This study introduces a real-time optimal scheduling method based on the Soft Actor-Critic (SAC) algorithm, aimed at reducing system operating costs and enhancing the consumption rate of renewable energy. First, the established mathematical model is converted into a Markov Decision Process (MDP), the objective function is converted by designing the action state space and the reward function, and the deep reinforcement learning framework is established. Finally, the decision-making outcomes of intelligence in various energy storage scenarios of renewable energy consumption and extreme cases are analyzed and compared, and the results show that the heat storage and hydrogen storage system significantly improve the rate of renewable energy consumption and the economy of the system. This method is also shown to significantly improve the rate of renewable energy consumption.

Optimal allocation of multiple energy storage in the integrated energy system of a coastal nearly zero energy community considering energy storage priorities

Energy storage technologies play a vital role in the low-carbon transition of the building energy sector. However, integrating multiple energy storage (MES) into integrated energy system (IES) in high-demand coastal communities remains a challenging task. This study proposes a novel regional IES that incorporates batteries, compressed air energy storage, and thermal energy storage for the simulated coastal community in Hong Kong; then developed the multi-objective optimization considering matching, economic, and environmental performance on MES capacity allocation with specially consideration of three energy management strategies under different energy storage priorities. The results show that all three storage priorities perform favorable outcomes across all objectives; compared to a reference scenario without storage, they achieve a relative net present value (NPVr) over 40 million HKD, a 0.29 improvement in matching performance, and over 2600 tCO2 in carbon emission reduction. Moreover, the renewable energy consumption rate exceeds 90 %, and the annual electricity import demand reduction rate exceeds 50 %. Sensitivity analysis highlights that larger energy storage capacities do not necessarily yield better outcomes, emphasizing the importance of synergistic interactions among multiple storage technologies. The presented energy system with MES and the optimal allocation approach herein may provide valuable insights for future energy planning in nearly-zero energy communities.

Two-stage low-carbon optimal dispatch of the power system considering demand response to defend large uncertainties and risks

Introduction: In order to promote the consumption of renewable energy, reduce carbon emissions, and take into account the uncertainty of renewable energy output and load fluctuations in the new power system that can affect the normal operation of market mechanism, a two-stage low-carbon optimization scheduling method for power system that considers demand response under multiple uncertainties is proposed in this paper.Methods: Uncertain scene sets are generated through Latin hypercube sampling and heuristic synchronous backpropagation method is used to reduce scenes to obtain typical scenes and their probabilities. Then, a one-stage optimization model is established with the goal of maximizing energy efficiency and corresponding demand response strategies are obtained. Green certificate carbon trading joint mechanism model consisting of tiered green certificate trading and time-sharing tiered carbon trading are established, and the output of two-stage units are optimized with the goal of minimizing comprehensive operating costs.Result: The simulation results show that the carbon emissions are decreased by 251.57 tons, the consumption rate of renewable energy is increased by 8.64%, and the total costs are decreased by 124.0612 million yuan.Discussion: From this, it can be seen that the dual layer low-carbon optimization scheduling strategy for power system considering demand response under multiple uncertainties can effectively reduce the operating costs and carbon emissions of the system, while balancing the economic and environmental aspects of power system operation.

Evaluating the contribution of demand response to renewable energy exploitation in smart distribution grids considering multi-dimensional behavior-driven uncertainties

Demand Response (DR) is recognized as an efficient method for reducing operational uncertainties and promoting the efficient incorporation of renewable energy sources. However, since the effectiveness of DR is greatly influenced by consumer behavior, it is crucial to determine the degree to which DR programs can offer adaptable capability and facilitate the use of renewable energy resources. To address this challenge, the present paper proposes a methodological framework that characterizes the uncertainties in DR modeling. First, the demand-side activities within DR are segmented into distinct modules, encompassing load utilization, contract selection, and actual performance, to enable a multifaceted analysis of the impacts of physical and human variables across various time scales. On this basis, a variety of data-driven methods such as the regret matching mechanism is introduced to establish the analysis model to evaluate the impact of various factors on DR applicability. Finally, a multi-attribute evaluation framework is proposed, and the effects of implementing DR on the economic viability and environmental sustainability of distribution systems are examined. The proposed framework is demonstrated on an authentic regional distribution system. The simulation results show that compared to scenarios without considering uncertainty, the proposed method can fully consider the impact of DR uncertainty, thereby enabling a more realistic assessment of the benefits associated with DR in enhancing renewable energy accommodation for smart distribution grids. From the comparative analysis of new energy installation scenarios, with the integration of photovoltaic and wind power into the system, the presence of DR can increase the renewable energy consumption rate by 6.39% and 37.44%, respectively, and reduce the system operating cost by 1.37% and 3.32%. Through the comparative analysis of different load types, when DR is a shiftable load and a two-way interactive load, the renewable energy consumption rate increases by 20.57% and 26.35%, and the system operating cost decreases by 2.12% and 4.68%. In this regard, the proposed methodology, hopefully, could provide a reliable tool for utility companies or government regulatory agencies to improve power sector efficiency based on a refined evaluation of the potential for demand-side flexibility in future power grids incorporating renewable energies.

Low‐carbon economic optimal operation strategy of rural multi‐microgrids based on asymmetric Nash bargaining

AbstractThe multi‐microgrids operation can greatly improve the proportion of renewable energy integration and power reliability of rural microgrids through energy trading between adjacent microgrids. Therefore, this paper proposes an optimization method for the low‐carbon economic operation of rural microgrids which contain wind power, photovoltaic, biogas, and other common rural renewable energy sources. Using the asymmetric Nash bargaining model which can form the non‐linear energy mapping function to quantify the bargaining power of each microgrid to establish the multi‐microgrids energy trading model, and the non‐convex trading optimization problem is equivalently transformed into two convex optimization subproblems, subproblem 1 is microgrids alliance costs containing the emission costs, and subproblem 2 is cooperative benefits distribution. Then, the accelerated Alternating Direction Method of Multipliers (ADMM) method is used to solve subproblem 1 and subproblem 2, respectively, and the privacy of transaction information is also protected. The results show that: compared with the single‐microgrid operation, the combined operation of rural multi‐microgrids with biogas can greatly improve the renewable energy consumption rate, reduce carbon emission, and improve the profit income of the alliance and each microgrid. Meanwhile, the accelerated ADMM algorithm used can greatly improve the solution speed.

Operation optimization of electricity-steam coupled industrial energy system considering steam accumulator

Steam system plays a crucial role in industrial energy usage. Steam generation in the industry domain is transferring from coal-fired or gas-fired plant/boiler to green-electricity steamer for net-zero purpose. The increasing coupling of the electricity-steam energy system in the industry domain, called electricity-steam coupled industrial energy system (ES-IES), brings enormous challenges to the system day-ahead operation optimization due to the uncertainties of renewable generation and the nonlinearities of the system mathematical model. Therefore, this paper introduces the steam accumulator (SA) into the steam system to mitigate the renewable fluctuations, proposes an isothermal linearization method of the steam flow model, and optimizes the operation based on an interactive iteration scheme between optimization and simulation for the global optimum and a balance between accuracy and speed. Results show that considering the storage characteristics of SA and the complementary coordination of electricity and steam through coupling equipment can significantly optimize the operation of ES-IES with an increase in the renewable energy consumption rate by 23.81 % and a decrease in the total operating cost by 11.39 %. The static payback period for the investment of EB and SA is 7 years, indicating a favorable application prospect.

Identifying the functional form and operation rules of energy storage pump for a hydro-wind-photovoltaic hybrid power system

Coupling energy storage pumps with conventional hydropower plants is one of the most valuable methods to increase the consumption rate of renewable energy. There are few small-scale hybrid power systems configurating energy storage pumps in the world (e.g., Ikaria Island, Greece). However, whether the operating principle and configuration method are feasible and transferable for a large-scale renewable energy base is unclear. This study proposes specific operating principles and configuration method for a large-scale hybrid power system, demonstrating the feasibility by investigating typical scenarios of an engineering case in Qinghai Province, China. Assume the conventional hydropower plants, wind farms, and PV stations in the case are constructed and in operation completely. The configuration relationship between energy storage pump and hydropower is investigated by setting the unit of energy storage pump from 1 to 50, the per-kW investment cost from CNY5000/kW to CNY30000/kW under the constraint of individual capacity of 100 MW. Furthermore, the economic indicators of internal rate of return and dynamic payback period are introduced to evaluate the performance of the whole hybrid power system. The configuration consequence of 18–21 pumps shows a good relation with renewable energy. This study provides theoretical and technical support for planning relevant hybrid power station projects.

Research on the Optimization of Energy–Carbon Co-Sharing Operation in Multiple Multi-Energy Microgrids Based on Nash Negotiation

Efficient and low-carbon energy utilization is a crucial aspect of promoting green and sustainable development. Multi-energy microgrids, which incorporate multiple interchangeable energy types, offer effective solutions for low-carbon and efficient energy consumption. This study aims to investigate the sharing of energy and carbon in multiple multi-energy microgrids (MMEMs) to enhance their economic impact, low-carbon attributes, and the efficient utilization of renewable energy. In this paper, an energy–carbon co-sharing operation model is established, incorporating carbon capture systems (CCSs) and two-stage power-to-gas (P2G) devices within the MMEMs to actualize low-carbon operation. Furthermore, based on cooperative game theory, this paper establishes an energy–carbon co-sharing Nash negotiation model and negotiates based on the energy–carbon contribution of each subject in the cooperation as bargaining power so as to maximize both the benefits of the MMEM alliance and the distribution of the cooperation benefits. The case study results show that the overall benefits of the alliance can be increased through Nash negotiation. Energy–carbon co-sharing can effectively increase the renewable energy consumption rate of 8.34%, 8.78%, and 8.83% for each multi-energy microgrid, and the overall carbon emission reduction rate reaches 17.81%. Meanwhile, the distribution of the benefits according to the energy–carbon co-sharing contribution capacity of each entity is fairer.

Collaborative Optimization of Transmission and Distribution Considering Energy Storage Systems on Both Sides of Transmission and Distribution

With the high penetration of renewable energy resources, power systems are facing increasing challenges in terms of flexibility and regulation capability. To address these, energy storage systems (ESSs) have been deployed on both transmission systems and distribution systems. However, it is hard to coordinate these ESSs with a single centralized optimization, and the time-domain coupling constraints of ESSs lead to high optimization complexity and a time-consuming calculation process. In this regard, this paper proposes a hierarchical transmission and distribution systems coordinative optimization framework, considering the ESSs at both ends of the systems. The decoupling of the time-domain coupling constraints of ESSs is realized by the Lyapunov optimization. Furthermore, the decoupling mechanism is embedded in the iterative process of analytical target cascading (ATC). In addition, an ATC-based Lyapunov optimization (ATC-L) approach is proposed to solve the co-optimization problem of the operations of the transmission system with multiple connected distribution systems. Through a case study, it is verified that the proposed framework and the ATC-L approach can effectively reduce the system’s operational cost and improve the consumption rate of renewable energy.

Environmental impacts of green bonds in cross-countries analysis: a moderating effect of institutional quality

PurposeThis paper aims to investigate the impacts of green bond issuance on the environment while taking into account the moderating role of issuing countries’ institutional quality.Design/methodology/approachThe analysis is based on a longitudinal data set covering 171 countries and territories during 2007–2018. The authors rigorously account for endogeneity issues using two-stage least squares estimation and a set of instrumental variables for green bond issuance volume.FindingsThe overall results confirm the positive environmental impacts of green bonds in reducing carbon dioxide and greenhouse gas emissions, enhancing renewable energy consumption rate and accelerating the progress towards sustainable development goals (SDGs). However, these effects are contingent upon the levels of institutional development of the issuing countries in a way that green bond issuance only benefits the environment when the institutional quality has reached a minimum level.Practical implicationsThe results provide important policy implications for countries in their efforts to prevent environmental degradation and achieve SDGs.Originality/valueThis paper contributes to the existing literature by providing a macro-level evaluation of the environmental impact of green bonds, hence, enabling policy implications to be drawn for countries to achieve their SDGs. The analysis is more comprehensive using a wide range of indicators for environmental performance. To the best of the authors’ knowledge, this paper is also one of the first attempts to examine the moderating effect of institutions on the environmental impact of green bonds.

An energy scheduling method for clustering islands with shared power exchanging vessels

Pelagic clustering islands are rich sources of renewable energy. However, they are isolated by natural sea areas, making it difficult to realize the interconnectivity of energy generation and utilization among different types of clustering islands. This paper proposes the strategy of coordinated and optimal energy scheduling for pelagic clustering islands based on shared power exchanging vessels (SPEV). First, the energy interconnection model among different types of islands is analyzed based on the concept of SPEV sharing. Second, the space-temporal coupling of SPEV navigation is established by considering SPEV sharing transportation capacity and the charging-discharging state of mobile battery packs. On this basis, a clustering islands energy scheduling model is established to minimize the comprehensive operation costs of clustering islands, considering the battery pack charge–discharge constraints, SPEV navigation route constraints, and passenger traveling constraints. The established energy scheduling model fully explores the space-temporal energy transfer flexibility of SPEV considering meeting the travel demands of passengers. Finally, clustering islands in the South China Sea are considered in the case study, and the simulation results indicate that the proposed SPEV-based clustering islands energy scheduling strategy can effectively improve the renewable energy consumption rate and the operating economy of clustering islands.

Multi-energy collaborative optimization of park integrated energy system considering carbon emission and demand response

Park integrated energy system (PIES) has become a key link of efficient energy conservation and carbon emission reduction. This paper proposes a multi-energy collaborative optimization method of PIES considering carbon emission and demand response (DR). Firstly, the typical structure of the electricity-thermal-gas cogeneration PIES including combined heat and power (CHP), heat pump (HP) and energy storage (ES) is built. Secondly, a ladder carbon trading model for PIES considering carbon quota and actual carbon emission is established. On this basis, a multi-objective collaborative optimization model considering the operation cost, energy utilization efficiency and consumption rate of renewable energy is established, and the multi-objective problem is solved by a multi-objective particle swarm optimization algorithm (MOPSO). Then, taking a typical PIES as an example, the operation conditions of the system before and after DR are analyzed, and the results show that the established model can realize the economic and low-carbon operation and improve renewable energy consumption rate. The numerical results show that when participating in DR, the operation cost of PIES is reduced by 10.18% and the carbon emission is reduced by 3.41%. Finally, the impact of carbon trading price on the operation cost, energy utilization efficiency and consumption rate of renewable energy is analyzed.

Photovoltaic Power Prediction Based on EMD-BLS Model

While the energy consumption structure is changing in this world, the consumption rate of renewable energy is gradually increasing. This paper takes photovoltaic power as the research object, analyses the power curve characteristics in different climatic change situation and the correlation between different weather factors and photovoltaic(PV) power output, and then puts forward a photovoltaic power prediction model based on empirical mode decomposition-broad learning system (EMD-BLS). First, the PV power historical sequence needs to be reconstructed after pretreatment, then the EMD method is used to decompose the reconstructed output sequence. The broad learning system model is established for each subsequence. Finally, the prediction results of each sub-series are superimposed to get the prediction of photovoltaic generation. The model is tested with a genuine data of photovoltaic power generation in a region of our country. Compared with the traditional broad learning system (BLS), extreme learning machine (ELM), LSTM, and other prediction models, The prediction error of the proposed model is lower, which can effectively improve the prediction accuracy of photovoltaic power generation.

Evolutionary game- based optimization of green certificate- carbon emission right- electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market, the demands on government financial subsidies are gradually increasing. Thus, a joint green certificate- carbon emission right- electricity multi-market trading process is proposed to study the market-based strategy for renewable energy. Considering the commodity characteristics of green certificates and carbon emission rights, the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models. Furthermore, considering the irrational bidding behavior of energy suppliers in the actual electricity market, an evolutionary game based multi-market bidding optimization model is presented. Subsequently, it is solved using a composite differential evolutionary algorithm. Finally, the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.