Planning Of Energy Systems Research Articles

Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory.To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology.In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating.We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available.

Two stage robust planning of park integrated energy system considering low carbon

The development of integrated energy systems is a significant way of improving energy efficiency and encouraging renewable energy absorption. However, the uncertainty of renewable energy and loads raise severe challenges to integrated energy system planning. Besides, under the low carbon background, the relationship between reducing carbon emissions and increasing the absorption of renewable energy is not investigated clearly in the planning of the park integrated energy system. Thus, this paper proposes a two-stage robust planning method considering low carbon and uncertainty. Firstly, the input-output model of energy conversion equipment in the park integrated energy system is described. Secondly, the objective function and constraints and are set up. Specifically, the carbon emission limits and renewable energy penetration target are introduced in the constraints. On this basis, the two-stage robust planning model with min-max-min structure is established, after transformation, column-and-constraint generation is used for the solution. Case study shows the ability and cost of the park integrated energy system to promote low-carbon emission reduction and renewable energy consumption. Moreover, it proves that although there is some synergy between the low carbon target and the renewable energy consumption target, maximum renewable energy consumption does not mean minimum carbon emissions, and vice versa, and in some cases increasing renewable energy penetration does not necessarily reduce carbon emissions.

Modelling and Energy Management of an Off-Grid Distributed Energy System: A Typical Community Scenario in South Africa

Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development.

5th generation district heating and cooling network planning: A Dantzig–Wolfe decomposition approach

The adequate planning of district energy systems is a key process to provide communities with proper heating and cooling networks. To find cost-effective planning solutions, this decision-making process is usually formulated as mathematical model, which is optimized to determine where and how many assets should be installed in the network. However, this optimization problem is becoming more complex as district energy systems evolve and become more elaborate. For the 5th generation district heating and cooling (5GDHC) networks, this planning framework comprises multiple building energy systems, a thermal and electrical network as well as central heating and cooling units. As a result, the optimization problem associated with these circumstances can easily become intractable as the number of elements of the network increases. To alleviate this tractability problem, in this paper, a Dantzig–Wolfe approach is devised to decompose a mixed-integer linear program into multiple subproblems (for every building) and a master problem (thermal and electrical network and central units). A realistic case study based on a 5GDHC system in Germany is considered. For this case study, it is demonstrated that the proposed decomposition approach yields the same results attained by the original not decomposed problem while achieving significant gains in terms of scalability and computational times. More specifically, the decomposition approach reduces the computational time for districts with more than 10 buildings by in average 94%. This corroborates the potential of the proposed approach to improve the computational efficiency of models that will deliver cost-effective investment plans for 5GDHC networks.

SolarGAN: Synthetic annual solar irradiance time series on urban building facades via Deep Generative Networks

Building Integrated Photovoltaics (BIPV) is a promising technology to decarbonize urban energy systems via harnessing solar energy available on building envelopes. While methods to assess solar irradiation, especially on rooftops, are well established, the assessment on building facades usually involves a higher effort due to more complex urban features and obstructions. The drawback of existing physics-based simulation programs are that they require significant manual modeling effort and computing time for generating time resolved deterministic results. Yet, solar irradiation is highly intermittent and representing its inherent uncertainty may be required for designing robust BIPV energy systems. Targeting on these drawbacks, this paper proposes a data-driven model based on Deep Generative Networks (DGN) to efficiently generate stochastic ensembles of annual hourly solar irradiance time series on building facades with uncompromised spatiotemporal resolution at the urban scale. The only input required are easily obtainable fisheye images as categorical shading masks captured from 3D models. In principle, even actual photographs of urban contexts can be utilized, given they are semantically segmented. The potential of our approach is that it may be applied as a surrogate for time-consuming simulations, when facing lacking information (e.g., no 3D model exists), and to use the generated stochastic time-series ensembles in robust energy systems planning. Our validations exemplify a good fidelity of the generated time series when compared to the physics-based simulator. Due to the nature of the used DGNs, it remains an open challenge to precisely reconstruct the ground truth one-to-one for each hour of the year. However, we consider the benefits of the approach to outweigh the shortcomings. To demonstrate the model’s relevance for urban energy planning, we showcase its potential for generative design by parametrically altering characteristic features of the urban environment and producing corresponding time series on building facades under different climatic contexts in real-time.

Towards 100% renewable energy systems: The role of hydrogen and batteries

New challenges arise for the accurate modelling of energy systems with a high share of renewable energy. In this context, energy storage technologies become key elements to manage fluctuations in renewable energy sources and electricity demand. The aim of this work is to investigate the role of batteries and hydrogen storage in achieving a 100% renewable energy system. First, the impact of time series clustering on the multi-year planning of energy systems that rely heavily on energy storage is assessed. The results show good accuracy, even for a small number of representative days, which is necessary to limit the computational burden of the optimisation problem. Then, different configurations of carbon-free energy systems are considered by varying the energy storage solution: only-battery, only‑hydrogen, and hybrid scenarios. An island energy system based on photovoltaics and floating offshore wind turbines is used as a demonstrative case study. It is shown that the cost of the only-battery configuration is 155% higher than the cost of the hydrogen-based scenarios. The reason is that the long-term hydrogen-based storage, despite its low round-trip efficiency, avoids costly oversizing of batteries and wind turbines throughout the analysed period. In the selected case study, hydrogen storage reduces the total rated power of the wind farm by about 5 times compared to the only-battery system. Hydrogen-based solutions are therefore crucial in 100% renewable energy systems to achieve energy self-sufficiency in a cost-effective way.

Optimal planning of energy and water systems of a small island with a hourly OSeMOSYS model

Islands all over the world face common challenges connected to energy costs and greenhouse gas emissions. Thus, islands have been identified as perfect sites for implementing and testing innovative solutions to boost the green energy transition towards a sustainable and clean energy system. The supply of clean water is a major issue that affects small islands, and desalination, particularly Reverse Osmosis, represents a valid solution to this challenge. In this research, an energy system model is used to analyse long-term water and energy supply strategies of the tourist island of Favignana, Italy. The model is built with the Open Source long-term energy modelling tool OSeMOSYS at an hourly resolution. It considers both the potential synergies offered by Reverse Osmosis Desalination and the use of water storage to store the excess electricity when needed. The indirect emissions for the maritime transportation of goods and fuels (i.e., water and diesel) to the island are also accounted for. Different energy policies are compared to understand how a carbon tax, a limit on emissions and no policy would impact the long-term energy strategy of the island. The results show that a carbon tax that covers also the maritime transportation sector would lead to the lowest overall cumulative emissions. They additionally reveal that the contribution of emissions for maritime transportation of goods and fuels is relevant and cannot be neglected if a full decarbonisation has to be achieved. On the technological side, investment in a desalination plant is the most viable option in all cases. Finally, for the first time, OSeMOSYS is applied with hourly resolution and the results are compared with those obtained with lower time resolution showing that inaccuracies are found both for overall values and for the dispatching strategies.

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

Severe weather conditions not only damage electric power infrastructure, and energy systems, but also affect millions of users, including residential, commercial or industrial consumers. Moreover, power outages due to weather-related natural disasters have been causing financial losses worth billions of US dollars. In this paper, we analyze the impact of power outages on the revenue of electric power suppliers, particularly due to the top five weather-related natural disasters. For this purpose, reliable and publicly available power outage events data are considered. The data provide the time of the outage event, the geographic region, electricity consumption and tariffs, social and economic indicators, climatological annotation, consumer category distribution, population and land area, and so forth. An exploratory analysis is carried out to reveal the impact of weather-related disasters and the associated electric power revenue risk. The top five catastrophic weather-related natural disaster categories are investigated individually to predict the related revenue loss. The most influencing parameters contributing to efficient prediction are identified and their partial dependence on revenue loss is illustrated. It was found that the electric power revenue associated with weather-related natural disasters is a function of several parameters, including outage duration, number of customers, tariffs and economic indicators. The findings of this research will help electric power suppliers estimate revenue risk, as well as authorities to make risk-informed decisions regarding the energy infrastructure and systems planning.

Selection of energy storage technologies under neutrosophic decision environment

The variability of renewable energy can be addressed by incorporating energy storage technologies (ESTs). Multiple energy storage technologies are available for renewable energy with wide disparity in attributes. Reports regarding the attributes are conflicting and complex, which makes it a multi-attribute decision analysis problem. To address this issue, new decision support tools are needed to aid in selecting ESTs based on their technical, environmental, and economic characteristics. In this study, a neutrosophic data envelopment analysis (NDEA) technique is developed and applied to selection of ESTs considering neutrosophic uncertainty inherent in new technologies. The decision tool systematically evaluates EST options by considering the expert's degree of satisfaction for higher technical performance, degree of dissatisfaction for lower technical performance and degree of indeterminacy for attaining better technical performance. Two case studies adapted from literature are used to illustrate the decision support tool. The first case study, which is used to demonstrate the tool for EST selection, involves the selection of thermal energy storage options. The second case study involves eight ESTs with different mechanisms: supercapacitor, flywheel, compressed air energy storage, pumped hydro energy storage, Li-ion battery, NiCd battery, NaS battery, and Pb-acid battery. The results reveal certain ESTs that are preferred over wide range of expert's perception to uncertainty in terms of dissatisfaction and indeterminacy. For example, Li-ion battery is DEA efficient if the risk tolerance for indeterminacy is greater than 40% and Pb-acid battery is DEA efficient at falsity tolerance greater than 80% and indeterminacy tolerance of less than 5%. These alternatives can be regarded as robust choices. Insights gained from the application of the decision tool can be used for guiding investment decisions in planning of renewable energy systems with energy storage.

Representative operating scenario selection with algebraic multi-grid clustering for integrated energy systems planning

Recent shifts towards higher renewable penetration in power systems, has resulted in increasing gas-fired generation capacity in systems without hydro or other fast responding alternatives, implying increased electricity and gas infrastructure interdependency. Furthermore, highly efficient combined heat and power (CHP) units lead to heat and electricity interdependency. It is crucial to consider these interdependencies in the expansion planning of energy systems for an effective and reliable investment planning and policy design. In this paper, the problem of integrated expansion planning of electricity, heat, and gas in presence of demand and wind uncertainty is addressed. Representative operating scenario selection with algebraic multi-grid clustering is used to model wind and demand uncertainties, and to address computational complexity of a central planning model. The simulation results on a modified IEEE-118 bus test system with a 14-node gas network shows that the algebraic multi-grid clustering outperforms the classical methods such as kmeans by following the benchmark case more closely.

Sustainable Energy Systems Planning, Integration and Management (Volume II)

Affordable and clean energy is one of the sustainable development goals (SDGs) introduced by the United Nations that are required to be followed by all developed and developing countries [...]

Interval-parameter bi-level programming for energy system management under uncertainty: Towards a deep-decarbonized and sustainable future in China

To reach the goal of carbon-neutral society, synergistic approaches based on mathematical models that are capable of planning energy system associated with complex uncertainty and hierarchical structure are desired. In this study, an interval-parameter bi-level programming (IBP) method that is effective for solving hierarchical decision-making problems with conflict objectives and uncertainty is developed. Then, an IBP based China's energy system model (abbreviated as IBP_CES) is formulated for mainland China during 2021–2050, where the higher-level objective of minimizing carbon dioxide (CO2) emission is given priority, following by the lower-level objective of minimizing system cost. Results from IBP_CES show that, although China's energy supply would still rely heavily on fossil fuels, the share of coal would decrease to 33.9% by 2050. Results also disclose that the national CO2 emission would reach the peak value during 2026-2030 and then decline rapidly due to development of renewable energy and promotion of carbon-treatment technology which is beneficial for government to achieve carbon neutrality goals. In addition, compared to the traditional single-level model, CO2 emission from IBP_CES can decrease by 5.9%, demonstrating that IBP_CES is more superior in energy system decarbonization. The findings can help decision makers to well understand the interactions and tradeoffs between climate mitigation and economic development, and provide effective information on clean, sustainable and deep-decarbonized development strategies.

A generalised optimal design methodology for distributed energy systems

The optimal combination of energy conversion and storage technologies with local energy demand is a key but in its result not obvious challenge of distributed energy. Although a variety of possible approaches to the optimal design of limited technology selections can be found in the literature, the previous design step, the actual technology selection, and the subsequent step, the selection of the optimal operating strategy, are often neglected. We develop and demonstrate a methodology, which can optimise energy systems with arbitrary technology selection and under multi-criteria optimality definitions. The energy system modelled in oemof.solph is optimised using a MOEA/D approach with regard to economic, ecological and technical key performance indicators. The aim is to find trends and tendencies with a methodology that is as generalised as possible in order to integrate it into the decision-making process in energy system planning. We demonstrate the method by means of a German district for which an integrated supply concept is being sought. Different evaluation and visualisation possibilities are presented and the chances and limitations of the developed methodology are identified. We show that not only the choice of technology, but especially its sizing and operational strategy have a decisive influence on the optimality.

Assessing flexibility for integrating renewable energies into carbon neutral multi-regional systems: The case of the Chilean power system

Reducing emissions from power systems requires enhancing the penetration of non-conventional renewable energy sources (NCRE) in the generation mix. However, such penetration requires high levels of operational flexibility in order to ensure an adequate balance between generation and demand. Concentrating solar power plants with thermal storage (CSP-TES) and battery energy storage systems (BESS) have shown to possess technical characteristics compatible with such high flexibility requirements. However, due to the high capital costs of these technologies, decision-makers must seek for cost-effective configurations and operation modes. This study presents the development of a methodological framework for designing the long-term transition of a multi-regional energy system towards a low carbon emission system. The sought system is characterized by a high penetration of NCRE, and the use of CSP-TES, BESS and electricity transmission settings for providing effective levels of operational flexibility. For this, the transformation of the Chilean electricity system between the years 2018–2050 is studied, using a tailored modification of the well-known OSeMOSYS optimization tool for energy systems planning. The main results indicate that by 2050, and considering a baseline scenario defined for 2016, for most of the scenarios studied the renewable electricity generation would be at least a 90 % and CO2 emissions would be 75 % lower. Furthermore, it is shown that providing operational flexibility to the system requires a mixed generation from hydroelectric reservoirs, CSP-TES plants, BESS, pumped-storage hydropower and natural gas generators. The obtained results allow planning the capacity and operation of CSP and BESS plants, which are adapted to the future flexibility requirements of the Chilean electric power system. Incentive policies like stimuli to growth BESS, would favor primarily the photovoltaic growth of the system at the expense of CSP-TES capacity, while CSP-TES growth incentives would maintain photovoltaic generation levels, but would decrease Wind and natural gas generation.

Rising targets to 55% GHG emissions reduction – The smart energy systems approach for improving the Italian energy strategy

The EU Commission raised the GHG emissions reduction targets from 40% to at least 55% by 2030, By the “Fit for 55” package. Member States have to review their national energy and climate plans (NECPs) in a short-time. In order to elaborate guidelines for the Italian NECP improvement, a methodology based on the Smart Energy Systems (SES) approach has been proposed. The reference scenario has been modelled in EnergyPLAN. A Sectors’ Implementation Matrix, consisting of eight sectors and three implementation levels, has been developed and the configurations have been simulated by means of MAT4EnergyPLAN. The best scenario accomplishes the 55% emissions reduction targets along with a lowering in the annualised costs of Italian energy system. The RES capacity targets must be increased by further 33 GW for PV and 16.6 GW for WIND. High end-uses electrification degree represents the priority in the next decade, so as to easily and cost-effectively integrate the variable-RES. An integrated energy system and the sector coupling even take priority over the RES installation in order to minimise the energy system decarbonisation costs. Finally, the present work contributes to demonstrate the need for the SES approach in the planning of energy systems with large-scale RES integration.

A complete total-factor CO2 emissions efficiency measure and “2030•60 CO2 emissions targets” for Shandong Province, China

Improving efficiencies in CO 2 emissions plays an important role in achieving carbon neutrality, but often overlooked in the planning and transformation of energy systems. Although data envelopment analysis (DEA) models are widely used for measuring efficiency, few papers have paid attention to whether the selected variables properly reflect the process of real-life production. This paper presents improvements to the existing input-output variable system of total-factor CO 2 emissions efficiency (TFCEE). The underlying research uses a slacks-based measure (SBM) with undesirable outputs to evaluate industrial TFCEE in Shandong Province, China, during the period 2006–2017. Based on current potential for CO 2 emissions abatement, a roadmap is proposed for the “2030•60 targets”. We ascertained that the main engine of net zero CO 2 emissions is efficiency improvement initially, shifting to structural adjustment by 2035. By 2037, carbon capture, utilization, and storage (CCUS) technologies are expected to be the second largest emission reducer, following structural adjustment. Based on a correct variable selection for CO 2 emissions efficiency measurement and the exploration of a carbon emission reduction roadmap, this study provides both empirical evidence and policy implications for achieving carbon peak and carbon neutral targets in industrially developed regions such as Shandong Province.

Impact of CO2 reduction targets on the integrated planning of municipal multi-energy carrier systems with grid infrastructures and energy conversion units

National climate protection goals require the emission reduction in municipal energy supply systems. Those systems include energy conversion units like heatings, energy storages, and grids for electricity, gas, and heat. This paper presents a mixed-integer linear optimization model for the integrated and multi-modal planning of such municipal energy systems to reach a defined CO2 emission limit with the least-cost system design. The exemplary application of the model to a study case with 3900 buildings shows that the flexible hybrid energy system resulting without emission restrictions gradually transforms into an inflexible system solely relying on pellet heaters and photovoltaic units when tightening the emission limit by just a few percentage points.

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

Electrification Planner for Ghana Using Open-Source Web GIS

Energy system planning provides information, such as electrification rate and access, essential to match demand and supply of energy. As countries strive to rapidly grow their economies and increase the living standards of its citizens, energy planning system is essential to keep track of assets, accessibility, adequacy, availability, and distribution of energy resources across locations targeted for development. In Ghana, the Ghana Energy Development and Access Project (GEDAP) was mandated to provide up to 100 % electrification rate to the citizens by 2020. While this time has elapsed, public information system showing the spatial distribution and statistical analysis of electrification rate and access in communities and local administrative areas remain scanty. Such decision support system, which can inform energy investment decisions and policy formulation by local and international investors is not readily available, impeding on the Government of Ghana’s (GOG) electrification expansion efforts. It also hinders the nation in attaining the United Nation’s (UN) Sustainable Development Goal (SDG) 7. Thus, the aim of this study was to develop a decision support system on electrification rate in Ghana. The study used energy access data and open-sourced Geographical Information System (GIS) to map the spatial distribution and provide statistical analysis of electrification rate in the country. The resulting information was connected to a WebGIS that can provide access to query, manipulate, and visualize electrification rate in the counting. The developed system estimated that, presently, Ghana has an electrification rate of 85.16 % as of November 2020. This information, and the system in general, will aid decision makers to make swift decision and provide geospatial evidence-based report in achieving 100 % electrification rate in the country.

Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties

District energy systems (DES) can reduce CO2 emissions associated with buildings while meeting the energy needs of a group of buildings with fossil fuel or renewable energy resources that are located on-site. One of the present challenges of DES is optimizing the operation of energy components, as different optimization methods are available. These optimization methods can have various requirements for implementation, distinct needs for engineering labor, and may rely on freely accessible software or proprietary software. Most importantly, different methods may result in dissimilar operation planning for a given DES, which makes the selection of optimization method a key consideration for decision-makers. In this study, two optimization methods, a mixed-integer linear programming (MILP) solver as a classical method and a non-dominated sorting genetic algorithm II (NSGA-II) as a metaheuristic method, are used to optimize the early-stage operation planning of a hypothetical DES for a university campus in a cool and dry climate. The objective is to minimize the operating cost and CO2 emissions when considering uncertainties in energy demands, solar irradiance, wind speed, and annualized electricity-related emissions. Both methods present similar operation of energy components, operating cost, and operating CO2 emissions. The MILP solver and NSGA-II algorithm vary in computation time to perform the optimization, initial knowledge to run the simulation, accessibility (free/open-source status), and satisfaction of constraints. This work compares the characteristics of a MILP solver and NSGA-II algorithm to help future researchers select the suitable optimization method related to their case study. The software underlying this work is open-source and publicly available to be reused and customized for early-stage operation planning of their specific DES. This work is novel by optimizing the operation planning of a mixed-used DES to minimize the cost and CO2 emissions while considering uncertainties in weather parameters, energy demands, and annualized electricity-related emissions.