Energy-Efficiency Economics as a Resource for Energy Planning

The objective of this work is a multi-criteria decision-making assessment that aims to facilitate the Energy-Efficiency Economics, introducing the Analytic Hierarchy Process (AHP) as part of power-system planning tool for an energy-efficiency application. It addresses to include qualitative aspects in the decision-making agendas of energy-efficiency projects. The manuscript details the limitations of non-rigorous financial analysis and proposes an alternative for including energy-efficiency measures in discussions pertaining to the financial opportunities available to any investor, and it presents the methodology that supports the qualitative aspects and the software package used to execute this methodology. As case study a complete example including a sensitivity analysis is presented.

Deep excavation of the impact from endogenous and exogenous uncertaintieson long-term energy planning

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study,we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linearprogramming (MILP), Monte Carlo simulation, and machine learning for quantification of the uncertaintyimpacts on a national-level energy system from global and local perspectives. This approach allows in-depthcorrelation analysis highlighting potential synergies and risks in the energy transition, and can be easily applie for assisting policy making. The case study for Switzerland shows that both carbon neutrality (even negativeemissions) and energy autonomy can be achieved by 2050, but the energy system’s configuration variessignificantly under uncertainty. Through conditional distribution analyses, carbon capture and storage (CCS),Photovoltaic (PV), and wood gasification show the most strong correlation for decarbonization. This study isbased on the whole uncertainty space taking into account heterogeneous uncertainties, leading to increasedreliability compared to sensitivity analysis from single scenarios’ comparisons. The synergy between energymodels and artificial intelligence (AI) is promising to be widely applied in energy planning area, particularlyfor emerging technologies with large uncertainty in development.

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

The household sector consumes roughly 30% of Earth's energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO_2 emissions, which are associated with the world's rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption.Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures(e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model "Evo Energy" to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making.

Big Data to support sustainable urban energy planning:The EvoEnergy project

Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO emission targets.This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system(EvoEnergy)using the old UK panel data survey,namely,the British household panel data survey(BHPS).In particular,the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study(UKHLS)launched in 2009.To achieve this aim,the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test.The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy(MAPE;5%)when compared to actual energy performance certificate data.From this premise,we recommend researchers,who are working on data-driven energy consumption forecasting,to consider merging the BHPS and UKHLS data sets.This will,in turn,enable them to capture the bigger picture of different energy phenomena such as fuel poverty;consequently,anticipate problems with policy prior to their occurrence.Finally,the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

With increasing renewable energy utilization,the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans.This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas.First,we provide a detailed formulation to calculate the renewable energy demand based on total energy demand.Second,we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems,after which we apply the differential evolution algorithmto solve the optimization result.Finally,we conduct a case study in Qingdao,China,to demonstrate the effectiveness of this optimizationmodel.Compared to the baseline design,the proposedmodel reduced annual costs and annual carbon emissions by 14.39%and 72.65%,respectively.These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions.Overall,this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.

Energy Planning of Beijing Towards Low-carbon,Clean and Efficient Development in 2035

Energy transition towards clean,efficient energy supply has been a common sense of the government and public in China.However,lacking reasonable planning will lead to undisciplined development,resource waste,and excessive investment.In this context,this paper investigates potential pathways of Beijing energy transition towards a high-level low-carbon,clean and efficient energy system in 2035 with an extended energysocpe model.Firstly,based on available data,future energy demands are predicted by a newly proposed hybrid forecasting method,which combines the traditional regression model,grey model,and support vector machine model with an entropy-based weighted factor.Secondly,the superstructure-based optimization model is employed to investigate the system configuration and operation strategy of the future Beijing energy system.Finally,the uncertainty impact of electricity price,natural gas price,hydrogen price,and the capital expenditures of electrolyzer and steam methane reforming for hydrogen applications are studied.The forecasting results show that all walks of life will witness a continuously increasing energy demand in multiple sectors of Beijing towards 2035.The planning results suggest that the imported electricity and natural gas will dominate the energy supply of Beijing in 2035 with a contribution of 86%of the energy resources consumption of 384 TWh.Moreover,the energy system presents a high end-use electrification level of 65%and high penetration of efficient technologies,which supply 119 TWh via combined heat and power,26 TWh via heat pump and 95 TWh via district heating network.The energy use of various sectors of energy resources,technologies and end-use are closely related.Hydrogen will have an increased penetration in the private mobility sector,but the locally generated hydrogen is mainly from steam methane reforming technology.

Economic Dispatch Considering Hourly Capacity Allocation with a Variable Renewable and Hydro-Based Generation Portfolio

The objective of this paper is to assess an economic dispatch considering a power system portfolio, which includes predominant amount of hydro power and increasing quantities of intermittent renewables in relation to the total electric capacity. With growing importance of intermittent wind and solar generation taking part into power systems worldwide, there is need for greater chronological resolution to estimate the flexibility of the power system to offer firm capacity. In this way, a linear optimization model operating hourly is developed to calculate the minimum power system cost, while stablishing the capacity allocation to meet the projected load throughout one-year simulation, as an estimation of how the hourly economic dispatch impacts the scheduling of generators belonging to a power system with this portfolio composition. A central focus is how to operate the available hydro capacity to back up intermittent renewables, evaluating the physical hydro operating constraints, monthly energy balance and maximum power availability. A case study was simulated based on the Brazil’s power system configuration, showing that existing hydro capacity provide hourly flexibility to back-up intermittent renewables, potentially saving 1.2 Billion R$, about 3.6% of total system cost referred to 2019. It is worthwhile to realize that the developed methodology can be employed to other power systems with similar capacity portfolio structure for the purpose of calculating its optimum allocation for a specified region and target year.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

Potential Impact of Biomass Cogeneration Plants on Achieving Climate Neutrality of BIH until 2050

The paper analyzes the potential of Biomass Combined Heat and Power (BCHP) plants in Bosnia and Herzegovina (BiH) in achieving climate neutrality until 2050. Two scenarios for reducing GHG emissions from the power generation sector in BiH until 2050 were developed. Scenarios were developed using LEAP, a software tool for energy policy analysis and climate change mitigation assessment. The complete final energy consumption and existing primary energy mix in BiH were included. Both scenarios imply a significant reduction in electricity generation from coal-fired power plants (CFPP). The first scenario (S1) involves the construction of a substitute CFPP unlike the second scenario in which there is no construction of a new CFPP, but part of the reduction in electricity generation from the CFPPs is compensated by BCHPs. The second scenario (S2) achieves a significantly higher reduction in GHGs emissions and provides an answer to the question of how much wood biomass is needed for the operation of BCHP for enabling the decarbonization of the power generation sector by 2050. S1 also represents a step toward reducing GHG emissions. Emissions from power generation in 2030 are about 60% lower than in 2015, i.e. by closing part of the existing CFPPs fleet, while in 2050 GHG emissions will be reduced by 12.26 million tons of CO2eq compared to 2015. The main advantage of S2 is the gradual phase-out of CFPPs and construction of BCHPs, which means incomparably lower GHG emissions, negligible in 2050, representing a key argument for the deployment of biomass potential for power generation. The technical potential of unused wood biomass in BiH is 7.44 PJ annually or 620,620 t annually. These quantities would be sufficient for the levels of electricity production in Scenario 2 by 2035. After that, the existing available technical potential is not enough. This means that BiH needs to increase biomass production and its technical potential to enable the implementation of that scenario.

Review of Energy Management and Planning of Islanded Microgrids

This survey paper provides a critical overview of optimization formulations for planning and operation of islanded microgrids,including optimization objectives,constraints,and control variables.The optimization approaches reviewed address methods both for increasing the resiliency of advanced distribution systems and electrification of remote communities.This paper examines over 120 individual optimization studies and discovers that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions,14 constraints,and 13 control variables.Each of the objectives,constraints,and variables are discussed exhaustively both from the perspective of their importance to islanded microgrids and chronological trends in their popularity.

Accommodating Uncertain Wind Power Investment and Coal-fired Unit Retirement by Robust Energy Storage System Planning

Increasing wind power integration and coal-fired unit retirements increases the strain on the power system’s spinning reserve and increases the pressure on peak regulation.With the ability to stock extra power generation and supply the peak load,the energy storage system(ESS)can alleviate the rising demand on the spinning reserve and play an increasingly important role in the power system.In this paper,a trilevel robust ESS planning model is proposed to accommodate uncertain wind power investment as well as coal-fired unit retirement.The upper-level of this model is to determine the planning scheme of ESSs,which iteratively takes the worst-case scenario of wind power investment and coal-fired unit retirement into consideration.The middle-level and lower-level of this model are to make the optimal daily economic dispatch under the worst-case realizations of uncertainties.We derive an equivalent reformulation of the proposed robust ESS planning model and solve it with a dual column-and-constraint generation algorithm.Case studies are conducted using the IEEE RTS-79 system.The results demonstrate the superiority of the proposed planning method in comparison with other methods.Furthermore,the effects of the capital cost of ESS,the expected proportion of wind power,and the uncertainty budget on the development of ESS are studied.Taking the uncertainties of unit retirement and wind power investment into consideration achieves a better trade-off between the ESS investment cost and the operational cost.

Energy Security Planning for Hydrogen Fuel Cell Vehicles in Large-Scale Events:A Case Study of Beijing 2022 Winter Olympics

Energy security planning is fundamental to safeguarding the traffic operation in large-scale events.To guarantee the promo-tion of green,zero-carbon,and environmental-friendly hydrogen fuel cell vehicles(HFCVs)in large-scale events,a five-stage planning method is proposed considering the demand and supply potential of hydrogen energy.Specifically,to meet the requirements of the large-scale events’demand,a new calculation approach is proposed to calculate the hydrogen amount and the distribution of hydrogen stations.In addition,energy supply is guaranteed from four aspects,namely hydrogen produc-tion,hydrogen storage,hydrogen delivery,and hydrogen refueling.The emergency plan is established based on the overall support plan,which can realize multi-dimensional energy security.Furthermore,the planning method is demonstrative as it powers the Beijing 2022 Winter Olympics as the first“green”Olympic,providing both theoretical and practical evidence for the energy security planning of large-scale events.This study provides suggestions about ensuring the energy demand after the race,broadening the application scenarios,and accelerating the application of HFCVs.

Modelling and analysis of decentralized energy systems with photovoltaic, micro-hydro, battery and diesel technology for remote areas of Nepal

Remote areas of Nepal suffer from limited or no access to electricity.Providing electricity access in remote areas is one of the foremost challenges of any developing country.The purpose of this study is to develop and propose a reliable and low-cost model for electrification.The study presents an optimized choice between decentralized renewable-energy systems and grid expansion.Opting for an analytical method for the modelling and analysis of electrification options based on life-cycle cost(LCC)and economic distance limit,each energy system for varied load conditions is compared for a better option.A framework for energy-system selection based on available resources is proposed.It compares the grid-expansion option with potential isolated renewable-energy systems to ensure energy access to the area under consideration.Additionally,off-grid configurations that rely on renewable energy sources are also considered for the necessity of backup supply to ensure continuous power to the research area.Techno-economic assessment is carried out for different off-grid and hybrid configurations proposed in this study and their feasibility checks are carefully examined.Commercial efficacy of the proposed hybrid energy systems is assessed by comparing the life cycle and energy cost and by performing different additional sensitivity analyses.The study concludes that reduced generation cost supports the increasing penetration of electrification.The LCC for grid expansion is the most economical under high-load conditions,whereas for the isolated and sparsely settled populations with low-load conditions,photovoltaic power backed up with a diesel generator is the most economical.

Launching Plan B:Biomass Energy

China’s first biomass electricity company focuses on helping farmers as it strives to expand In April 2011,the world’s largest biomass power company,China National Bio Energy Co.Ltd.(NBE),began building a biomass power plant in Shangcai County of central China’s Henan Province.The new plant,due to