Browse
Subjects
Records with Subject: Planning & Scheduling
Showing records 132 to 156 of 1331. [First] Page: 1 3 4 5 6 7 8 9 10 11 Last
Optimization Method for Operation Schedule of Microgrids Considering Uncertainty in Available Data
Hirotaka Takano, Ryota Goto, Ryosuke Hayashi, Hiroshi Asano
April 19, 2023 (v1)
Keywords: balance of power supply and demand, economic load dispatch (ELD), microgrids, operation schedule of microgrids, particle swarm optimization (PSO), treatment of uncertainty, unit commitment (UC)
Operation scheduling in electric power grids is one of the most practical optimization problems as it sets a target for the efficient management of the electric power supply and demand. Advancement of a method to solve this issue is crucially required, especially in microgrids. This is because the operational capability of microgrids is generally lower than that of conventional bulk power grids, and therefore, it is extremely important to develop an appropriate, coordinated operation schedule of the microgrid components. Although various techniques have been developed to solve the problem, there is no established solution. The authors propose a problem framework and a solution method that finds the optimal operation schedule of the microgrid components considering the uncertainty in the available data. In the authors’ proposal, the objective function of the target problem is formulated as the expected cost of the microgrid’s operations. Since the risk of imbalance in the power supply a... [more]
Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review
Giovanni Rinaldi, Philipp R. Thies, Lars Johanning
April 19, 2023 (v1)
Keywords: condition monitoring, condition-based maintenance, digitalisation, fault diagnosis/prognosis, floating wind, Industry 4.0, O&M planning, offshore renewable energy, robotics, SCADA, soft sensors
Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and... [more]
An Interval Optimization-Based Approach for Electric−Heat−Gas Coupled Energy System Planning Considering the Correlation between Uncertainties
Wenshi Wang, Houqi Dong, Yangfan Luo, Changhao Zhang, Bo Zeng, Fuqiang Xu, Ming Zeng
April 19, 2023 (v1)
Keywords: affine coordinate transformation, correlations model, EH multi-objective interval optimization, Genetic Algorithm, uncertainties
In this paper, a novel methodological framework for energy hub (EH) planning, considering the correlation between renewable energy source (RES) and demand response (DR) uncertainties, is proposed. Unlike other existing works, our study explicitly considers the potential correlation between the uncertainty of integrated energy system operations (i.e., wind speed, light intensity, and demand response). Firstly, an EH single-objective interval optimization model is established, which aims at minimizing investment and operation costs. The model fully considers the correlation between various uncertain parameters. Secondly, the correlation between uncertainties is dealt with by the interval models of multidimensional parallelism and affine coordinate transformation, which are transformed into a deterministic optimization problem by the interval order relationship and probability algorithm, and then solved by a genetic algorithm. Finally, an experimental case is analyzed, and the results sho... [more]
Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm
Carolina Gil Marcelino, Carlos Camacho-Gómez, Silvia Jiménez-Fernández, Sancho Salcedo-Sanz
April 19, 2023 (v1)
Keywords: bio-inspired algorithms, coral reefs optimization algorithm, Energy Efficiency, generation scheduling, hydro-power plants, meta-heuristics
Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem. This approach uses different search operato... [more]
Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers
Kaixuan Ji, Ce Chi, Fa Zhang, Antonio Fernández Anta, Penglei Song, Avinab Marahatta, Youshi Wang, Zhiyong Liu
April 19, 2023 (v1)
Keywords: cooling system, data center, energy-aware, marginal cost, task classification, task scheduling
The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy redu... [more]
An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid
Sajjad Ali, Imran Khan, Sadaqat Jan, Ghulam Hafeez
April 19, 2023 (v1)
Keywords: battery energy storage systems, demand response, energy management, photovoltaic, Scheduling, smart grid
Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is r... [more]
Scheduling Optimization of a Cabinet Refrigerator Incorporating a Phase Change Material to Reduce Its Indirect Environmental Impact
Angelo Maiorino, Adrián Mota-Babiloni, Manuel Gesù Del Duca, Ciro Aprea
April 19, 2023 (v1)
Keywords: carbon emission, carbon intensity, cooling, Optimization, phase change materials (PCMs), thermal energy storage (TES)
Phase Change Materials (PCMs) incorporated in refrigerators can be used to shift their energy consumption from peak periods, when the electric network energy demand is the highest, to off-peak periods. While PCMs can flatten the energy demand curve, they can achieve economic savings if Time-of-Use (TOU) electricity tariffs are applied. However, the hourly carbon emission factor is not commonly linked to the hourly tariff, and the final CO2 emitted due to the operations of the refrigerator would not be fully optimized. In this work, a method based on the Simulated Annealing optimization technique was proposed to identify the optimal working schedule of a cabinet refrigerator incorporating a PCM to reduce its indirect carbon emissions. Data from countries with different representative carbon intensity profiles were used. The normalized standard deviation and normalized range are the best statistical indexes to predict carbon emission reduction in the proposed solution. These parameters p... [more]
Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning
Ying Ji, Jianhui Wang, Jiacan Xu, Donglin Li
April 19, 2023 (v1)
Keywords: data driven modeling, microgrid energy management, proximal policy optimization, recurrent neural network
The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is desig... [more]
Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning
Ce Chi, Kaixuan Ji, Penglei Song, Avinab Marahatta, Shikui Zhang, Fa Zhang, Dehui Qiu, Zhiyong Liu
April 19, 2023 (v1)
Keywords: cooling system, data center, deep reinforcement learning, Energy Efficiency, multi-agent, scheduling algorithm
The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers t... [more]
Optimal Planning and Operation of a Residential Energy Community under Shared Electricity Incentives
Pierpaolo Garavaso, Fabio Bignucolo, Jacopo Vivian, Giulia Alessio, Michele De Carli
April 19, 2023 (v1)
Keywords: electricity sharing, energy community, energy hub, multi-energy, Optimization
Energy communities (ECs) are becoming increasingly common entities in power distribution networks. To promote local consumption of renewable energy sources, governments are supporting members of ECs with strong incentives on shared electricity. This policy encourages investments in the residential sector for building retrofit interventions and technical equipment renovations. In this paper, a general EC is modeled as an energy hub, which is deemed as a multi-energy system where different energy carriers are converted or stored to meet the building energy needs. Following the standardized matrix modeling approach, this paper introduces a novel methodology that aims at jointly identifying both optimal investments (planning) and optimal management strategies (operation) to supply the EC’s energy demand in the most convenient way under the current economic framework and policies. Optimal planning and operating results of five refurbishment cases for a real multi-family building are found a... [more]
Decarbonizing the Energy System of Non-Interconnected Islands: The Case of Mayotte
Anna Flessa, Dimitris Fragkiadakis, Eleftheria Zisarou, Panagiotis Fragkos
April 18, 2023 (v1)
Keywords: decarbonization, energy system planning tools, energy transition pathways, Mayotte, non-interconnected islands, RES penetration
Islands face unique challenges on their journey towards achieving carbon neutrality by the mid-century, due to the lack of energy interconnections, limited domestic energy resources, extensive fossil fuel dependence, and high load variance requiring new technologies to balance demand and supply. At the same time, these challenges can be turned into a great opportunity for economic growth and the creation of jobs with non-interconnected islands having the potential to become transition frontrunners by adopting sustainable technologies and implementing innovative solutions. This paper uses an advanced energy−economy system modeling tool (IntE3-ISL) accompanied by plausible decarbonization scenarios to assess the medium- and long-term impacts of energy transition on the energy system, emissions, economy, and society of the island of Mayotte. The model-based analysis adequately captures the specificities of Mayotte and examines the complexity, challenges, and opportunities to decarbonize t... [more]
Research on Data-Driven Optimal Scheduling of Power System
Jianxun Luo, Wei Zhang, Hui Wang, Wenmiao Wei, Jinpeng He
April 18, 2023 (v1)
Keywords: deep reinforcement learning, grid dispatching optimization, importance sampling, proximal policy optimization algorithm
The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback−Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example show... [more]
Opening of Ancillary Service Markets to Distributed Energy Resources: A Review
Francesco Gulotta, Edoardo Daccò, Alessandro Bosisio, Davide Falabretti
April 18, 2023 (v1)
Keywords: aggregator, ancillary service, balancing service provider, distributed energy resources, market models
Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according... [more]
Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data
Reda El Makroum, Ahmed Khallaayoun, Rachid Lghoul, Kedar Mehta, Wilfried Zörner
April 18, 2023 (v1)
Keywords: Genetic Algorithm, home energy management, load scheduling, user comfort
This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demons... [more]
Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning
Rasheed Abdulkader, Hayder M. A. Ghanimi, Pankaj Dadheech, Meshal Alharbi, Walid El-Shafai, Mostafa M. Fouda, Moustafa H. Aly, Dhivya Swaminathan, Sudhakar Sengan
April 18, 2023 (v1)
Keywords: Distributed Power Generation, Energy Storage System, Renewable and Sustainable Energy, Smart Grid, soft computing
Distributed Power Generation and Energy Storage Systems (DPG-ESSs) are crucial to securing a local energy source. Both entities could enhance the operation of Smart Grids (SGs) by reducing Power Loss (PL), maintaining the voltage profile, and increasing Renewable Energy (RE) as a clean alternative to fossil fuel. However, determining the optimum size and location of different methodologies of DPG-ESS in the SG is essential to obtaining the most benefits and avoiding any negative impacts such as Quality of Power (QoP) and voltage fluctuation issues. This paper’s goal is to conduct comprehensive empirical studies and evaluate the best size and location for DPG-ESS in order to find out what problems it causes for SG modernization. Therefore, this paper presents explicit knowledge of decentralized power generation in SG based on integrating the DPG-ESS in terms of size and location with the help of Metaheuristic Optimization Algorithms (MOAs). This research also reviews rationalized cost-b... [more]
Optimal Incorporation of Intermittent Renewable Energy Storage Units and Green Hydrogen Production in the Electrical Sector
Tania Itzel Serrano-Arévalo, Javier Tovar-Facio, José María Ponce-Ortega
April 18, 2023 (v1)
Keywords: energy demand, Energy Storage, green hydrogen, Optimization, Planning
This paper presents a mathematical programming approach for the strategic planning of hydrogen production from renewable energies and its use in electric power generation in conventional technologies. The proposed approach aims to determine the optimal selection of the different types of technologies, electrolyzers and storage units (energy and hydrogen). The approach considers the implementation of an optimization methodology to select a representative data set that characterizes the total annual demand. The economic objective aims to determine the minimum cost, which is composed of the capital costs in the acquisition of units, operating costs of such units, costs of production and transmission of energy, as well as the cost associated with the emissions generated, which is related to an environmental tax. A specific case study is presented in the Mexican peninsula and the results show that it is possible to produce hydrogen at a minimum sale price of 4200 $/tonH2, with a total cost... [more]
Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty
Kalyani Makarand Kurundkar, Geetanjali Abhijit Vaidya
April 18, 2023 (v1)
Keywords: contingency, load-following, multiperiod, security-constrained, stochastic, uncertainty
In the context of the growing penetration of renewable power sources in power systems causing probabilistic contingency conditions, a suitable economic dispatch model is decisively needed. There is a lack of research in the field of probabilistic mathematical formulation considering the uncertainties due to the stochastic nature of renewables and contingency occurrence, as it is a very complex problem to be solved. The most appropriate model is the stochastic security-constrained economic dispatch (SSCED) model for optimized economic dispatch decisions during uncertainty. However, because of its complexity, it is rarely employed. This paper attempts to solve the complex SSCED problem in the presence of the uncertainty of resources and probabilistic contingency conditions, which is a novel effort in this regard. The SSCED is carried out over multiple periods to provide the load-following or contingency reserves. In the proposed SSCED, the uncertainty problem is addressed by modeling the... [more]
Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan
Hasnain Iftikhar, Nadeela Bibi, Paulo Canas Rodrigues, Javier Linkolk López-Gonzales
April 18, 2023 (v1)
Keywords: decomposition methods, electricity consumption, monthly forecasting, times series models
In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumptio... [more]
The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales
Tianyao Zhang, Weibin Huang, Shijun Chen, Yanmei Zhu, Fuxing Kang, Yerong Zhou, Guangwen Ma
April 17, 2023 (v1)
Keywords: hybrid system, load curve, Scheduling, self-adaptive, source-load matching, ultra-short-term
Establishing a wind-solar-hydro hybrid generation system is an effective way of ensuring the smooth passage of clean energy into the grid, and its related scheduling research is a complex and real-time optimization problem. Compared with the traditional scheduling method, this research investigates and improves the accuracy of the scheduling model and the flexibility of the scheduling strategy. The paper innovatively introduces a sand-table deduction model and designs a real-time adaptive scheduling algorithm to evaluate the source-load matching capability of the hybrid wind-solar-hydro system at ultra-short-term scales, and verifies it through arithmetic examples. The results show that the proposed adaptive sand-table scheduling model can reflect the actual output characteristics of the hybrid wind-solar-hydro system, track the load curve, and suppress the fluctuation of wind and solar energy, with good source-load matching capability.
A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power
Khalid A. Alnowibet, Ahmad M. Alshamrani, Adel F. Alrasheedi
April 17, 2023 (v1)
Keywords: bilevel programming, KKT conditions, market power, mixed-integer linear/quadratic programming, stochastic programming, transmission expansion planning
Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The... [more]
Novel Planning Methodology for Spatially Optimized RES Development Which Minimizes Flexibility Requirements for Their Integration into the Power System
Bojana Škrbić, Željko Đurišić
April 17, 2023 (v1)
Keywords: constrained least squares, decarbonization, flexibility, Optimization, RES capacity expansion planning
An optimization model which determines optimal spatial allocation of wind (WPPs) and PV power plants (PVPPs) for an energy independent power system is developed in this paper. Complementarity of the natural generation profiles of WPPs and PVPPs, as well as differences between generation profiles of WPPs and PVPPs located in different regions, gives us opportunity to optimize the generation capacity structure and spatial allocation of renewable energy sources (RES) in order to satisfy the energy needs while alleviating the total flexibility requirements in the power system. The optimization model is based on least squared error minimization under constraints where the error represents the difference between total wind and solar generation and the referent consumption profile. This model leverages between total energy and total power requirements that flexibility resources in the considered power system need to provide in the sense that the total balancing energy minimization implicitly... [more]
Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm
Jiankai Gao, Yang Li, Bin Wang, Haibo Wu
April 17, 2023 (v1)
Keywords: automated machine learning, collaborative optimization, multi-agent deep reinforcement learning, multi-microgrid
The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system’s operating cost. Ad... [more]
Research on Supply Chain Optimization Considering Consumer Subsidy Mechanism in the Context of Carbon Neutrality
Zheng Liu, Wenzhuo Sun, Bin Hu, Chunjia Han, Petros Ieromonachou, Yuanjun Zhao, Jiazhuo Zheng
April 17, 2023 (v1)
Keywords: carbon emissions, consumer subsidies, pricing strategy, trading mechanism
With the proposal of China’s “carbon peak, carbon neutral” strategy, the increasing awareness of low carbon production among consumers, and the government’s introduction of carbon trading mechanism and low carbon consumption subsidy policies, enterprises are facing good opportunities for development. However, how the government can reasonably formulate low carbon policies and how enterprises can implement optimal low-carbon production decisions are still key issues in China’s low-carbon transition development. In this context, this paper is based on the carbon trading mechanism and carbon consumption subsidies. In this context, based on the carbon trading mechanism, this paper focuses on green production and green consumption, considers the impact of low-carbon consumer preferences and government subsidies on enterprises’ low-carbon production decisions, and uses the optimal theory to study the optimal pricing strategy and the optimal carbon reduction strategy. The study shows that the... [more]
Energy Supply Systems Predicting Model for the Integration of Long-Term Energy Planning Variables with Sustainable Livelihoods Approach in Remote Communities
Carlos Pereyra-Mariñez, José Andrickson-Mora, Victor Samuel Ocaña-Guevera, Félix Santos García, Alexander Vallejo Diaz
April 17, 2023 (v1)
Keywords: energy management, energy systems optimization, evolutionary algorithm, Renewable and Sustainable Energy, sustainable livelihoods
The Sustainable Development Goals (SDGs) of the United Nations Organization pursue the provision of affordable and quality energy for all human beings, which is why the correct planning of Energy Supply Systems (ESS) in communities that present levels of energy poverty, that is, the impossibility to satisfy their minimum needs for energy services. This work proposes a methodology to evaluate the contribution to development by the adequate provision of the demand of ESS in remote communities through the approach of Sustainable Livelihoods (SLs). The methodology starts from the initial evaluation of the sustainable livelihoods or capitals of the communities and the analysis of their interaction. Then, a capital improvement process is proposed by selecting the indicator values that optimize the model in each period, through an evolutionary algorithm that guarantees that the indicators evolve to a rich scenario as a result of planning to evolve the key variables based on a quantitative mod... [more]
Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid
Wengang Chen, Jiajia Chen, Bingyin Xu, Xinpeng Cong, Wenliang Yin
April 17, 2023 (v1)
Keywords: Energy Storage, industrial park microgrid, multi-transformer, optimal configuration, user-side
Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is develop... [more]
Showing records 132 to 156 of 1331. [First] Page: 1 3 4 5 6 7 8 9 10 11 Last
[Show All Subjects]