Planning Of Energy Systems Research Articles

Developing a multi-objective multi-layer model for optimal design of residential complex energy systems

Optimal planning of residential complex energy systems requires thorough mathematical modelling to address the interconnections between all the energy installations from the largest ones, shared by all the residents, to the smallest ones in each distinct unit. Besides, conflicting desires of investors and residents in various aspects such as reliability index make this problem more challenging. In response, this paper presents a thorough framework to obtain the optimum design and operation of a residential complex energy system from scratch. To address the appropriate interconnection between various components of such an energy system, a multi-layer energy hub structure is proposed. Besides, the proposed optimization problem consists of a multi-objective function to consider several criteria such as reliability, operational costs, and investment costs to address both investors’ and residents’ desires appropriately. A Multi-objective Particle Swarm Optimization (MOPSO) algorithm is implemented to obtain the optimum solutions for the developed optimization problem, and a set of Pareto optimal solutions are introduced. By doing so, investors can accurately analyse the interrelation among different objectives such as reliability, investment costs, and operational costs of the residential complex and choose an optimum solution based on their priorities. To demonstrate the effectiveness of the proposed method, it is applied to a case study consisting of a residential energy system with 300 units. The numerical results indicate that such a framework not only reduces total operation and investments costs by 24% but also increases reliability, continuity of energy supply, significantly in comparison to other common methods such as single-layer EH and single-objective EH planning frameworks.

Optimal Power Dispatch in Energy Systems Considering Grid Constraints

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, an energy system dispatch optimization model is employed. It includes an iterative approach for generating grid constraints, which is decoupled from the linear unit commitment problem. The dispatch of all energy carriers in the system is optimized while considering the physical electrical grid limits. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, a PV penetration of ∼5 kWp per household can lead to curtailment of ∼60 MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors and reduces the energy curtailment up to 45%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safe and optimal operation of a complex energy system is fulfilled. Efficient control strategies and more accurate plant sizing could be derived from this work.

Assessment of Wind and Solar Hybrid Energy for Agricultural Applications in Sudan.

In addition to zero-carbon generation, the plummeting cost of renewable energy sources (RES) is enabling the increased use of distributed-generation sources. Although the RES appear to be a cheaper source of energy, without the appropriate design of the RES with a true understanding of the nature of the load, they can be an unreliable and expensive source of energy. Limited research has been aimed at designing small-scale hybrid energy systems for irrigation pumping systems, and these studies did not quantify the water requirement, or in turn the energy required to supply the irrigation water. This paper provides a comprehensive feasibility analysis of an off-grid hybrid renewable energy system for the design of a water-pumping system for irrigation applications in Sudan. A systematic and holistic framework combined with a techno-economic optimization analysis for the planning and design of hybrid renewable energy systems for small-scale irrigation water-pumping systems is presented. Different hybridization cases of solar photovoltaic, wind turbine and battery storage at 12 different sites in Sudan are simulated, evaluated, and compared, considering the crop water requirement for different crops, the borehole depth, and the stochasticity of renewable energy resources. Soil, weather, and climatic data from 12 different sites in Sudan were used for the case studies, with the key aim to find the most robust and reliable solution with the lowest system cost. The results of the case studies suggest that the selection of the system is highly dependent on the cost, the volatility of the wind speed, solar radiation, and the size of the system; at present, hybridization is not the primary option at most of sites, with the exception of two. However, with the reduction in price of wind technology, the possibility of hybrid generation will rise.

Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty

Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China’s multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China’s energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO2 emission in the future; (iv) compared with the single-level model, the CO2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development.

Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy

This paper introduces the exergy efficiency that takes into account both the quantity and quality of energy, and constructs a bi-level planning optimization model of the regional integrated energy system. Factors such as equipment exergy efficiency are considered by the upper-level planning model. Through matter-element information theory and Frequent Patterm-growth algorithm, the energy structure of the regional integrated energy system planning is determined by quantitative means. The lower-level planning model is intended for economy and exergy efficiency, and the tabu search algorithm is embedded in the solving algorithm of the multi-objective genetic algorithm for solving, determining the capacity of each equipment in the energy structure. In the case study, three comparison schemes are used to verify the proposed model and method through the regional integrated energy system in a resort town in northern China. Among them, Scheme 1 is devised for economy, Scheme 2 is proposed for economy and energy efficiency, and Scheme 3 is intended for economy and exergy efficiency. The results show that compared with the Scheme 1 and Scheme 2, the system exergy efficiency of Scheme 3 corresponding to this model has increased by 17.34% and 11.17%, respectively. Despite an increase of 10.61% in total annualized cost in Scheme 3 compared to Scheme 1, there is a decrease of 6.37% compared to Scheme 2. Despite the conflict in improving exergy efficiency and economy, there is still a balance to draw in the proposed model in this paper.

The model construction in airport renewable energy system scale planning based in Multi-Criteria Decision Making

In order to solve the energy consumption and environmental problems, renewable energy was utilized in many airports. However, due to the periodic and intermittent, a single renewable energy generation is difficult to provide a continuous supply all the time. Hybrid energy system was considered to be the important way to alleviate the intermittent. The study focuses on the optimal design of hybrid renewable energy system planning. A model based on Multi-Criteria Decision Making (MCDM) was constructed. This model could achieve the optimization of scale for renewable energy and energy storage devices in hybrid renewable energy. The model was built in consideration of different power grid operation strategies including “Independence-net operation” and “grid connected operation”. The results showed that the environmental and social benefits could be calculated at the same time, and the optimal renewable energy system scale planning outcome could be obtained.

Evaluating risk propagation in renewable energy incidents using ontology-based Bayesian networks extracted from news reports

ABSTRACT Accidents in the booming renewable energy industries can negatively impact people, animals, and buildings in social systems and ecosystems. Many studies focus on the renewable energy incidents in a single wind-based, solar-based, or hydro-based system, and lack holistic views of incident risk management covering all the systems. This paper proposes a framework for evaluating the comprehensive and cross-system renewable energy incidents by ontology-based Bayesian networks extracted from incident reports using text mining methods. An ontology for renewable energy incidents involving 34 classes, 241 instances, and 352 properties is first created based on knowledge and a hand-annotated dataset with 2 237 event statements. Then the ontology is used to filter and map risk factors and their relations extracted from 11 504 renewable energy incident news reports into nodes and edges in a multi-graph. Finally, the multi-graph is utilized to learn Bayesian networks for modeling renewable energy incident risk propagation, which is evaluated and analyzed by Bayesian inference algorithms. The result shows that there are significant and widespread risk propagation effects in different renewable energy systems, especially the “Separation-Falling-Injury-Fatality” risk transmission chain in the wind-based systems. Fire, collision, deformation, wearing, and falling risks contribute the most to the consequences like damages, fatalities, and failure, and the combined effect of risk elements in the risk transmission chains is the deep-seated reason for generating those consequences for renewable energy incidents. The proposed framework provides new insights into risk analysis for the security planning and construction of renewable energy systems.

Temperature and flow rates dynamics in a district heating (DH) network fed by a combined heat-and-power (CHP) plant

Research focus (problem description, short background):The energy efficiency of a district heating (DH) system can be improved by fine-tuning supply and return temperatures and flow rates without need for invasive renovation of the end user heating systems. Research methods:We employ a correlation coefficient to examine the relationship between the local supply temperature and the use of heat; research relies on data hourly heat generation records from each heat generation facility in the DH system of Omsk, Russia, 2016–2017. Key research results/findings:The correlation declines the further we go from winter. Firstly, the relationship between the transition states and the outside temperature results from the relationship between external conditionings and heat parameters in DH. Secondly, both the energy consumption and the supply temperature are affected by the infrastructure, other weather conditions, and energy price; they are also highly dependent on consumer behavior, which is arbitrary, heterogeneous, and interdependent. Thirdly, the regional heating load is influenced by the characteristics of a building cluster. Although nearly all DH plants have a control system in place, we find that human factor jeopardizes the performance of such systems. Main conclusions and recommendations:To generalize these results to other systems, we emphasize that the effective diffusion coefficients and effective heat-loss coefficients are influenced by the thermal dynamics in the network and change on typical time scale (one year), hence their uncertainty. This study has produced a technique for visualizing the relationship that exists in a DH system between supply temperature and heat consumption; this technique is expected to contribute to the dynamic modeling, control, and planning of future integrated energy systems. The load profiles are dynamic; a thermal storage could be used to shift the timing of heating loads to minimize the flow through DH networks.

A sampling-based solution approach for electricity capacity expansion planning with chance constraint

In order to achieve carbon neutrality, several countries and regions have set specific targets for the reduction of carbon emissions. Although many policy makers agree on the uncertainty of the several factors which are involved in the planning of future energy systems, they have yet to consider the risks that their policies will incur, should they fail to meet current carbon emission targets. In this study, we propose a stochastic programming-based energy system model which takes into account various economic and technical uncertainty factors in addition to a chance-constraint, in order to assess the feasibility of meeting carbon emission targets. To determine the chance-constrained programming model efficiently, we propose a solution-based approach which adopts a sample average approximation approach. In addition, we combine the sampling-and-discarding approach with a newly devised heuristic algorithm that is suitable for our proposed model. Furthermore, as it is a numerical study, we apply our model and solution approach to evaluate the long-term capacity expansion plan under the carbon emission target within the Korean electricity sector. Through this numerical study, we verify both the solution quality and computational time of our solution approach, as well as the usability of our model for the trade-off analysis between costs and risks.

Energy and atmosphere system planning of coal-dependent cities based on an interval minimax-regret coupled joint-probabilistic cost-benefit approach

This study proposes a novel method titled interval minimax-regret coupled joint-probabilistic cost-benefit analysis for the energy and atmosphere system planning of coal-dependent cities located in ecologically fragile areas under multiple uncertainties. The suitability of the model is demonstrated by applying it to Yulin City, China within a 15-year planning horizon (2020–2034). Energy and atmosphere system management schemes have been defined under 63 different scenarios in the model. Results reveal that: (i) both energy supply for the manufacturing industry and pollutant emission would decrease as joint probability level increases; (ii) a higher possibility of violating the total secondary energy output level would lead to a lower energy supply for the manufacturing industry and pollutant emission; (iii) when primary energy output and secondary energy output increase by 5% and 20%, respectively, and production is produced as planned, the system would achieve the lowest cost-benefit ratio ([0.67, 0.80]) and the highest net present value rate ([0.25, 0.48]) from 2020 to 2034. The model supports decision-makers by providing innovative insights into the tradeoffs among system objectivity, reliability, and profitability.

A Dynamic Multi-Stage Planning Method for Integrated Energy Systems considering Development Stages

Integrated energy system (IES) planning is a long-term and rolling decision-making process. According to System Development Theory, the development-needs at different stages are different. Therefore, an IES dynamic multi-stage planning method considering different development stages is proposed. The first step of the method is putting forward a model based on the degree of system coupling, the reserve ratio, and the penetration rate of clean energy to divide dynamic development stages. Secondly, establishing a dynamic multi-stage planning model of the IES by combining the needs of different development stages through dynamic goals and constraints. Finally, the results given by the optimal configuration of critical IES equipment will be analysed in different scenarios. Following these steps, the result shows that the dynamic multi-stage planning method proposed is able to reduce the total planning cost of the system by 14% and reducing the clean energy penetration rate by 3%. Therefore, the proposed dynamic multi-stage planning scheme is effective and economical.

Planning urban energy systems adapting to extreme weather

• Urban energy systems are planned considering extreme weather events (EWE). • A flexible and efficient two-layer framework is developed with generality. • Increasing supply or deploying storage can ensure energy supply during EWE. • ‘Storage Prior’ heuristics requires 2.8% extra cost for deploying storage. • ‘Supply Prior’ heuristics needs 5.4% extra cost for expanding gas power capacity. In the context of increasing urbanization and climate change globally, urban energy systems (UES) planning needs adequate consideration of climate change, particularly to ensure energy supply during extreme weather events (EWE) such as heatwaves, floods, and typhoons. Here we propose a two-layer modeling framework for UES planning considering the impact of EWE. An application of the framework to a typical coastal city of Xiamen, China reveals that deploying energy storage (i.e., pumped hydro and battery) offers significant flexibility to ensure the critical demand is met during typhoon as a typical EWE and avoids over investment in supply technologies. This requires an extra 2.8% total cost on investment and operation of UES for 20 years. Planning energy systems with proper consideration of EWE can ensure robust urban energy services even with increasing penetration of fluctuating renewables, and we offer a flexible and computationally efficient paradigm for UES planning considering the impact of EWE.

Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations

The installation of fast electric vehicle charging stations (EVCS) will be essential to promote the acceptance by the users of electric vehicles (EVs). However, if EVCS are exclusively supplied by the grid, negative impacts on its stability together with possible CO2 emission increases could be produced. Introduction of hybrid renewable energy systems (HRES) for EVCS can cope with both drawbacks by reducing the load on the grid and generating clean electricity. This paper develops a weighted multicriteria methodology to design the most suitable configuration of HRES for EVCS. This method determines the local renewable resources and the EVCS electricity demand. Then, taking into account environmental, economic and technical aspects, it deduces the most adequate HRES power generation planning for EVCS. Besides, an experimental stage to validate the design deduced from the multicriteria methodology is included. Therefore, the final power generation planning for the HRES in EVCS is supported not only by a complete numerical evaluation, but also by an experimental verification of the demand being fully covered. Methodology application to Valencia (Spain) proves that an off-grid HRES with solar PV, wind and batteries support would be the most suitable configuration for the system. This solution was also experimentally verified.

Integrated Energy System Planning Optimization Method and Case Analysis Based on Multiple Factors and A Three-Level Process

Now that China has outlined its goals of “carbon peak and carbon neutrality”, the development of clean energy will accelerate, the connection between different energy systems will be closer, and the development prospects of the integrated energy service industry will be broader. Integrated energy services are promoting energy transformation and services. “Carbon peaking, carbon neutrality” and other aspects will also have multiple values and far-reaching significance. Before implementing integrated energy services, the top-level design of integrated energy system planning must be carried out, and how to achieve the optimal allocation of capacity in the field of integrated energy systems is an urgent problem to be solved in integrated energy system planning. This paper combines practical engineering experience and the latest theoretical research results to creatively introduce, for the first time, a comprehensive evaluation into the initial planning stage, and proposes, also for the first time, a three-level multi-element comprehensive energy system planning optimization method which combines multi-element requirements to carry out comprehensive energy system planning and optimization. The three-tier planning and optimization solution results in the optimal planning scheme of the integrated energy system, thereby making the scheme more specific and reliable. According to the demand data of an industrial park, this method was applied to complete a case analysis of integrated energy system planning, which verified the feasibility and effectiveness of the method. This method is easy to popularize, and it will guide the planning of integrated energy systems, promote integrated energy services, promote energy transition, and make positive contributions to achieve carbon neutrality as soon as possible.

Dual-stage attention-based long-short-term memory neural networks for energy demand prediction

Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector.

Building Energy System Design and Planning: The Universidad de Santander Case

Renewable generation is gaining more thrust as the time passes, however, the growth rate of such production has been faster than the rate of the electrical grid modernization. This growth comes with great opportunities and challenges for building energy system (BES) design and planning. In this respect, this paper presents the experience and lessons learned from the design and planning of the BES of the “Universidad de Santander” (UDES) central campus. This BES renewable generation is composed of a rooftop PV system distributed across the campus buildings. The system started with 30 kWp of installed capacity (100 kWh/day on average) and now is ready to deliver near 300 kWh/day thanks to 70 kWp of extra capacity installed. In general, the implementation has been a success that now is being replied in other UDES’ campuses, and the decision taking involved highlighted the importance of a proper energy efficiency policy and electrical regulation analysis.

Techno‐economic optimization of a grid‐connected hybrid energy system considering electric and thermal load prediction

AbstractOptimal planning of hybrid energy systems has always been a considerable task. While several studies have conducted techno‐economic analysis on hybrid energy systems, most of them have neglected to consider appropriate electric and thermal load growth rates. This paper proposes an efficient strategy for optimizing a grid‐connected hybrid energy system considering electric and thermal load growth rates. HOMER software is used for optimizing hybrid energy systems. It uses a nonderivative optimization to recognize the system with the minimum net present cost (NPC) among hundreds of configurations. The exponential smoothing method is also used for predicting upcoming load peak values based on historical data. The proposed hybrid system includes solar photovoltaic (SPV) system, wind turbine (WT) system, converter, battery storage system (BSS), gas generator (GasGen), fuel cell (FC), fuel‐fired boiler, and resistive boiler. Unlike previous studies, electricity price growth and SPV degradation rates are considered in this study. The grid‐connected system has also been compared to the stand‐alone system to understand grid connection impacts on the optimization results. Environmental analysis has been performed to analyze the greenhouse gas emissions of each system. The effects of growth rates in thermal and electric loads and electricity price are comprehensively investigated. Neglecting load growth rates affected the financial results severely. In this condition, the NPC and COE are achieved −11 383.4 US$ and −0.02376 US$/kWh. Results indicate that the combination of WT system (15 kW), converter (10 kW), resistive, and fuel‐fired (10 kW) boilers is the most economical configuration in grid‐connected mode. The WT system plays an essential role in the electric power provision and revenues of the hybrid system. The net present cost (NPC) and the cost of energy (COE) for grid‐connected operating mode are achieved −0.0162272 US$/kWh and −5243.956 US$, respectively. For off‐grid operating mode, the NPC and COE are obtained 47 024.19 US$ and 0.1983879 US$/kWh, respectively. Thanks to the feed‐in‐tariff (FiT) policy, the proposed grid‐connected system is more economical and environmentally friendly than the stand‐alone system. In off‐grid operating mode, 63 575.45285 kg CO2 is produced, which is higher than CO2 production in the grid‐connected mode (more than 8262 kg).

Multi-objective programming for energy system based on the decomposition of carbon emission driving forces: A case study of Guangdong, China

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role; (b) the coal-dominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure; (c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regional scale.

A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting

Countries around the globe have introduced renewable energies (RE) and minimized the dependency of fossil resources in power systems to address extensive environmental risks. However, such large-scale energy transitions pose a great challenge to power systems due to the volatility of RE. Meanwhile, power demand is increasing over time and it shows temporal characteristics, such as seasonal and peak-valley patterns. Whether the future power system with a larger proportion of RE can meet the surging but fluctuated electricity demand remains problematic. Previous studies on short-term load forecasting focused more on forecasting accuracy than stability. Further, there is a relative paucity of research into temporal patterns. In order to fill in these research gaps, this paper proposes a fuzzy theory-based machine learning model for workdays and weekends short-term load forecasting. Fuzzy time series (FTS) is applied for data mining and back propagation (BP) neural network is used as the main predictor for short-term load forecasting. To exploit the trade-offs between forecasting stability and accuracy, multi-objective optimization is applied to modify the parameters of BP. Moreover, an interval forecasting architecture with several statistical tests is constructed to address forecasting uncertainties. Short-term load data from Victoria in Australia is selected as a case study. Results demonstrate that the proposed method can significantly boost forecasting stability and accuracy, and help strategy making in the field of energy and electricity system management and planning.

An Interval Optimization-Based Approach for Electric–Heat–Gas Coupled Energy System Planning Considering the Correlation between 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 show that the research method in this paper has good engineering practicability. At the same time, different correlations among uncertainties have different influences on integrated energy system planning. Correlation and influence are positively correlated.