Consumption Rate Of Renewable Energy Research Articles

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.

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.

Design of Renewable Energy Consumption Scheduling Model Based on Quantitative Feedback Theory

In the renewable energy consumption scheduling, due to the large fluctuation of wind power output, the renewable energy consumption rate is low, and the energy consumption scheduling effect is poor. Therefore, a new renewable energy consumption scheduling model based on quantitative feedback theory is designed. For the first time, calculate the output response of wind power generation, obtain the output change rate of the wind farm at the time scale, and determine the response curve of wind power generation according to the power generation process is divided into three stages: the initial stage, the peak stage and the end stage, and the response output during the peak period, so as to obtain the transfer rate of output during the peak period. Build a mathematical model of renewable energy power generation, calculate the output state of wind power generation, and analyze the output characteristics of renewable energy storage system; Secondly, the objective weight coefficient of renewable energy consumption is determined by using quantitative feedback theory, the objective function of renewable energy consumption is constructed, the power injected by parameter nodes is determined, the quantitative feedback controller is designed by using loop shaping technology, the control structure of two degrees of freedom is determined, the objective function of renewable energy consumption is constructed, and the constraint range of renewable energy consumption capacity is determined according to static constraints such as current constraints and voltage constraints; Then, the quantitative feedback theoretical controller is designed, the input and output transfer functions of the consumption system are determined, and the renewable energy consumption scheduling model is constructed. The renewable energy consumption scheduling model is solved by particle swarm optimization through a variety of index parameters in the renewable energy consumption determined by the QFT controller. The experimental results show that the proposed model can effectively improve the renewable energy consumption rate and optimize the consumption scheduling effect.

Optimal Dispatch of Regional Integrated Energy System Group including Power to Gas Based on Energy Hub

Different renewable energy resources and energy demands between parks lead to waste of resources and frequent interactions between the regional distribution grid and the larger grid. Hence, an optimal dispatching scheme of the regional integrated energy system group (RIESG), which combines the power-to-gas (P2G) and inter-park electric energy mutual aid, is proposed in this paper to solve this problem. Firstly, for the park integrated energy system (PIES) with various structures, the coupling matrix is used to describe the input-output relationship and coupling form of multiple energy sources in the energy-hub (EH), which linearizes the complex multi-energy coupled system and is more conducive to the solution of the model. Secondly, the electrical coupling relationship of the system is improved by adding P2G to enhance the system’s ability to consume renewable energy. Moreover, the installation cost of P2G is introduced to comprehensively consider the impact of the economic efficiency on the system. Finally, to minimize the network loss of energy flow, the optimal dispatching model of RIESG with P2G conversion is constructed through the electric energy mutual aid among the parks. The simulation shows that compared with the independent operation of each park’s integrated energy system (IES), the proposed optimal dispatching strategy of RIESG achieves the mutual benefit of electric energy among park groups, reduces the dependency on the large power grid, and effectively improves the economy of system groups. In this condition, the renewable energy consumption rate reaches 99.59%, the utilization rate of P2G increases to 94.28%, and the total system cost is reduced by 34.83%.

An autonomous control technology based on deep reinforcement learning for optimal active power dispatch

The large-scale renewable energy integration has brought challenges to energy management in modern power systems. Due to the strong randomness and volatility of renewable energy, traditional model-based methods may become insufficient for optimal active power dispatch. To tackle the challenge, this paper proposes an autonomous control method based on soft actor–critic (SAC), a deep-reinforcement learning (DRL) strategy recently developed, which provides an optimal solution for active power dispatch without a mathematical model while improving the renewable energy consumption rate under stable operation. A Lagrange multiplier is introduced to the SAC (LM-SAC) to promote algorithm performance in optimal active power dispatch. A pre-trained scheme based on imitation learning (IL-SAC) is also designed to further improve the training efficiency and robustness of the DRL agent. Simulations on the IEEE 118-bus system with the open platform Grid2Op verify that the proposed algorithm effectively achieves better renewable energy consumption rate and robustness compared with existing DRL algorithms.

Factors influencing the adoption of renewable energy in the U.S. residential sector: An optimal parameters-based geographical detector approach

The use of fossil fuels has been a major contributor to global climate change. To mitigate the negative impact of fossil fuels, renewable energy (RE) has been increasingly promoted. This study aims to investigate factors impacting the adoption of RE in the residential sector, using the U.S. as a case study. Residential RE consumption rate data in 51 states/districts of the U.S. were collected from 2010 to 2018 to examine the adoption of RE. Correspondingly, four categories of explanatory variables affecting the consumption rate were investigated, including demographics, availability, consumers' affordability, and regulatory compliance motivation. An optimal parameters-based geographical detector (OPGD) model was implemented to investigate spatio-temporal impacts of variables on the consumption rate. Results show that factors that have consistently significant impacts on residential RE adoption include percentage of RE production of the availability category and residential monthly electricity bill of the consumers’ affordability category. Population was also an important factor from 2012 to 2018. The impact of RE production rate can also be further enhanced when combined with other factors, such as residential monthly bill and number of regulatory policies. Based on these results, several practical recommendations are proposed as references for government to increase RE adoption.

Research on planning optimization of integrated energy system based on the differential features of hybrid energy storage system

At present, it is very important to build the new power system with new energy as the mainstay. Some Regional Integrated Energy Systems (RIES) have installed renewable energy generation equipments with wind turbines and photovoltaics as the main power supply facilities. Due to the volatility in the wind turbine, photovoltaic output power and the customer's electricity load, there is energy waste in the RIES. In order to enhance the economic and renewable energy consumption rate of the park where the RIES is applied, an integrated energy system(IES) planning optimization model is constructed，which considers the mixed storage differentiation characteristics. The model expands the hybrid energy storage system(HESS) on the basis of the original RIES. A two-stage decomposition model combining wavelet packet decomposition method and power decomposition method based on discrete Fourier transform is established, in which the difference between power generation of renewable energy and customer load is decomposed. Depending with the difference in response speed of energy storage devices, the power of different frequencies is moderated. With the comprehensive energy system economy as the goal, the genetic algorithm based on elitist preservation strategy is adopted to optimize the planning model and verify the correctness of the model through simulation. The results show an improvement of 8.23 % and 23.79 % in economy and renewable energy consumption rate compared to the RIES before the HESS expansion.

Optimal scheduling method of integrated energy system in parks considering wind and solar energy consumption

Aiming at the problem that the renewable energy cannot be efficiently utilized and the energy consumption cost is high in the production process of the park with wind and solar energy, an integrated energy system (IES) for the park that takes into account the wind and solar energy consumption is proposed. Optimize the scheduling method. First, an output model of the park’s comprehensive energy system including wind-solar renewable energy was established, and biogas was used as the gas source. Then, considering a variety of energy constraints, and taking the lowest energy consumption cost and the highest wind and light absorption rate as the goal, the improved multi-objective particle swarm algorithm is used to optimize the solution. Finally, the proposed method is verified by taking an integrated energy system of a park as a practical example. The results show that the proposed optimal scheduling method can effectively reduce the daily operating cost of the park and improve the local consumption rate of renewable energy.

Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties

Energy storage (ES) is playing an increasingly important role in reducing the spatial and temporal power imbalance of supply and demand caused by the uncertainty and periodicity of renewable energy in the microgrid. The utilization efficiency of distributed ES belonging to different entities can be improved through sharing, and considerable flexibility resources can be provided to the microgrid through the coordination of ES sharing and demand response, but its reliability is affected by multiple uncertainties from different sources. In this study, a two-stage ES sharing mechanism is proposed, in which the idle ES capacity is aggregated on the previous day to provide reliable resources for real-time optimization. Then, a two-layer semi-coupled optimization strategy based on a deep deterministic policy gradient is proposed to solve the asynchronous decision problems of day-ahead sharing and intra-day optimization. To deal with the impact of multiple uncertainties, Monte Carlo sampling is applied to ensure that the shared ES capacity is sufficient in any circumstances. Simulation verifies that the local consumption rate of renewable energy is effectively increased by 12.9%, and both microgrid operator and prosumers can improve their revenue through the joint optimization of ES sharing and demand response.

Research on capacity optimization and real-time control of island microgrid considering time-shifting load

The utilization efficiency of renewable energy can be improved by the effective control of time-shifting load. In this paper, the characteristics of the time-varying energy source and load model including wind, diesel storage, and seawater desalination are studied. The optimal operation strategy of the island microgrid is established. The objective of this optimization model is to minimize the annual investment cost of joint functions. On this basis, to further improve the renewable energy consumption rate of the system, the real-time control optimization strategy under the operation of the island microgrid is studied. The calculations show that the real-time power prediction through wavelet packet neural network and the optimal allocation strategy of island microgrid capacity based on time-shifting load can improve the acceptance of renewable energy. In the long run, it can solve the problem of lack of fresh water on the island and help to improve the economy.