Future Energy Costs Research Articles

Will wind power be cost-effective for decarbonizing the city of Tabriz-Iran, whose economy is oil-based, and how?

This paper investigates the decarbonization of Tabriz-City focusing on its wind power for electrification. Statistical, quantitative, comparative, and simulation-research-methods used to analyze the existing and future total energy consumption, demand, and cost in the city according to carbon-based and wind-based electricity. The Monte Carlo Simulation Method has been used to estimate the probability of the Levelized Cost of Electricity. Tabriz thermal power plant generates 1kWh electricity that costs 0.15 US$/kW without subsidies and produces 575 g.CO2/kW.h overall while it will plunge to 0.05US$/kWh producing 7 g.CO2/kWh using wind. The NPV and IRR (32%) analysis show that that investment in wind-based electricity is three times cheaper than thermal power electricity in Tabriz. It is shown that the electrification of an oil-based economy with wind-based power plants is an economical investment for the city. Besides the hub-height and rotor sweep area, the capacity factor is the most decisive in the productivity of the alternative turbines.

Probabilistic economic evaluation of the most prevalent in Greek market space heating systems

In the present study, the most prevalent space heating (SH) systems are assessed by means of probabilistic economic method, for Athens (Greece) under the ongoing energy crisis. The examined SH systems are the air source heat pump, the natural gas boiler and the oil boiler. An apartment of 85 m2 living area is chosen as the standard Athenian dwelling type for the assessment of the SH systems. In addition, two economic scenarios are set related to the annual energy inflation rate, the pessimistic and the optimistic. Simulations are executed in TRNSYS for the physical system and in MATLAB for the economic analysis. The economic analysis is carried out with the combination of the Monte Carlo simulations and the Life Cycle Cost (LCC) method. It turned out that, the LCC (as net present value) for all systems is much higher than the pre-crisis cost and is not feasible under the existing obscure economic period. It was proved that, the energy cost subsidy may be a solution on the volatility of the energy market, however, this can be achieved with rather high subsidy rates. The air source heat pump system was found to obtain better economy than the conventional fuel-based systems and the pairing of the heat pump with the PV system proved as the only economic feasible solution. The developed methodology can be utilized for evaluating the investment risk associated with the uncertain of the future cost of energy.

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification.

Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.

To Control Grid Power Factor and Increase Energy Efficiency in Thermal Power Plant through Energy Audit: A Review

Abstract— Energy is one of the major inputs for economic development in any country in the world. In developing countries, energy is a major contributor and huge investment is required to meet the energy needs in such countries. India has been a dynamic country since its inception. There are many industries here where electricity consumption is mostly 60% to 65%. The main objective of this paper is to maintain optimum energy throughout the organization by focusing on those energy points to reduce current and future energy costs and improve power quality. This paper presents “To Control Grid Power Factor and Increase Energy Efficiency in Thermal Power Plant through Energy Audit”. In this energy audit analysis is done for a month to improve the power factor close to unity by the automatic reactive power control panel to maintain the grid p. f. to 0.99 by compensating for the required reactive power from the grid-connected alternator. . Energy audits will help you understand the energy and fuel systems used in any industry, and identify the various areas where energy loss can occur and where it has scope. The proposed approach is validated by considering the case study. Keywords—Audit and control of energy, Energy Consumption, Energy Conservation, Energy Savings, PF Control, Auto Reactive Power Control.

“Show Me the Energy Costs”: Short and Long-term Energy Cost Disclosure Effects on Willingness-to-pay for Residential Energy Efficiency

Imperfect information on future energy costs can lead households to underestimate the monetary savings of more energy efficient technologies, with implications for future energy supply, emissions and the climate. These hypotheses are explored using a randomised discrete choice experiment for property rental decisions. Results show that when energy consumption is expressed in physical units (as per the current EU labels) the willingness-to-pay for energy efficiency improvements is small and marginally significant. While bimonthly energy cost information has no effect on the valuation of energy efficiency, annual monetary energy cost information leads to a significant increase. This is the first paper to compare short and long-term point of sale energy cost information for household property decisions.There are clear implications for labelling policy for properties—framing energy consumption according to long-term monetary cost increases the demand for energy efficiency.

“Show Me the Energy Costs”: Short and Long-term Energy Cost Disclosure Effects on Willingness-to-pay for Residential Energy Efficiency

Imperfect information on future energy costs can lower the demand for more energy efficient technologies, with clear implications for energy supply, emissions and climate change. These hypotheses are explored using a randomised discrete choice experiment for property rental decisions. Results show that when energy consumption is expressed in physical units (as per the current EU labels) the will-ingness-to-pay for energy efficiency improvements is small and marginally significant. While bimonthly energy cost information displayed in monetary terms has no effect on the valuation of energy efficiency, annual monetary energy cost information led to a significant increase. This is the first paper to compare short and long-term point of sale energy cost information for household property decisions. There are clear implications for labelling policy for properties—framing energy consumption according to long-term monetary cost increases the demand for energy efficiency.

Estimating the impact of energy efficiency on housing prices in Germany: Does regional disparity matter?

The German government is aiming for a climate-neutral building stock before 2050 to meet the defined goals of the Climate Action Plan 2050. Increasing the building stock's energy efficiency is therefore a high priority, and investments by private homeowners will greatly influence this, as around 46.5% of German homes are owner-occupied. To identify the possible monetary benefits of investments in energy retrofits, we investigate whether energy efficiency is reflected in the property values of German single-family homes. Therefore, we examine potential heterogeneous effects across regions. With 422,242 individual observations on a 1 km2-grid level from 2014 to 2018, this study adds to the extant literature by 1) examining the energy efficiency effect on housing values for the entire country and specifically investigating regional disparities in this context, and 2) estimating an energy efficiency value-to-cost ratio to compare housing values' increase with initial investment costs and future energy cost savings. Applying hedonic analysis, we find a positive relationship between energy efficiency and asking prices. If energy efficiency increases by 100 kWh/m2a, prices increase by 6.9% on average. We also find evidence for regional disparities. The effects are significantly weaker in large cities than in other urban areas, whereas the impact in rural regions is much stronger. According to this, housing shortage and higher purchasing power per capita were identified as drivers for low energy efficiency premiums. Finally, there is evidence that about 98% of future energy cost savings are already reflected in a higher housing value under myopic expectations regarding future energy prices.

MPG Illusion and Vehicle Choice: An Empirical Study of the Japanese Household Survey

Previous studies have shown that people understand the future ancillary cost of durable goods such as an automotive vehicle. However, consumers tend to misunderstand the future benefits and costs of these goods. One of the most interesting discussions about consumer cognitive ability for future energy cost is the miles per gallon (MPG) illusion. In this study, we analyze people’s misunderstanding of the relationship between kilometers per liter (KPL) and the actual amount of fuel saved using vehicle owner survey data. We developed some questions to measure how much each person is involved with the MPG (or KPL) illusion. Additionally, our survey includes questions capturing some preferences affecting future fuel costs, such as time. Controlling for the most important respondent characteristics, such as income or gender, our empirical model analyzes the extent of the misunderstanding of how much actual KPL selections of personal auto vehicles are affected. We found that many Japanese consumers tend to misunderstand the relationship between KPL and actual fuel costs. Our results demonstrate that people who misunderstand the relationship tend to choose a higher 4.324 km per liter car than those who understand. This finding implies that the KPL illusion affects the KPL selection of consumers’ cars.

An Effective Energy Management With Advanced Converter and Control for a PV-Battery Storage Based Microgrid to Improve Energy Resiliency

This article presents an energy management strategy for a microgrid having solar PV arrays and a battery energy storage system (BESS). Most of the energy management strategy used for commercial photovoltaic (PV) inverters and battery inverters do not consider the future load behavior and cannot ensure the energy resiliency for a PV and battery storage based microgrid. This article proposes the use of a model predictive control strategy that considers future load behavior and energy cost profile to determine an optimum power flow trajectory to achieve energy resiliency, minimize the operating cost and maximize the profit of the microgrid. The proposed control strategy of the energy management system (EMS) is validated using both simulations in MATLAB/Simulink environment and by carrying out laboratory experiments on a developed laboratory test platform. For the converters of the test platform, this article develops a magnetically linked seven-level multilevel converter (acting as the solar PV inverter) and a full-bridge inverter with an advanced pulsewidth modulation technique (acting as the BESS inverter). Commercial inverters usually require large and bulky power frequency transformers for the grid integration of renewable energy sources. The high-frequency magnetic link-based power electronic converter can produce multiple balanced dc power sources with reduced size, ensures lower power losses during the energy conversion process and can also ensure galvanic isolation. The results from the simulation and experimental studies of the EMS demonstrate that the developed energy management strategy ensures both the minimization of the operating cost of the system and the increased resiliency of the whole system.

Uncertainty analysis of the future cost of wind energy on climate change mitigation

We conduct uncertainty analysis on the impacts of the future cost of wind energy on global electricity generation and the value of wind energy to climate change mitigation. We integrate data on global onshore and offshore wind energy cost and resources into the Global Climate Assessment Model (GCAM), and then propagate uncertainty based on distributions derived from an expert elicitation study on the future cost of onshore and offshore wind energy. The share of wind energy electricity generation in 2035, without a global policy on CO2 emissions, ranges between 4% and more than triple the 2019 share of 5.3%. Under a 1.5°C cap, this range is wider, with shares up to 34%. This range of uncertainty implies the need for flexible systems and policies, allowing large amounts to be deployed if needed. We explore whether a breakthrough in wind energy could prevent the demand for natural gas as a bridge technology to a low carbon economy, and find that uncertainty in wind energy is only pertinent for medium-stringency policies, such as a $60/t carbon tax. Under this scenario, there is a 95% chance that the cost of wind energy will be low enough to lead to an immediate reduction in the share of natural gas. In contrast, under a business-as-usual scenario without a breakthrough in cost, natural gas is highly likely to continue increasing in share of electricity generation. Under a 1.5°C cap, natural gas will decrease in share regardless of wind energy cost.

Machine Learning from Schools about Energy Efficiency

In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging.

Future Socio-Political Scenarios for Aquatic Resources in Europe: An Operationalized Framework for Aquaculture Projections

Climate-driven changes in aquatic environments have already started to affect the European aquaculture sector’s most commercially important finfish and shellfish species. In addition to changes in water quality and temperature that can directly influence fish production by altering health status, growth performance and/or feed conversion, the aquaculture sector also faces an uncertain future in terms of production costs and returns. For example, the availability of key ingredients for fish feeds (proteins, omega-3 fatty acids) will not only depend on future changes in climate, but also on social and political factors, thereby influencing feed costs. The future cost of energy, another main expenditure for fish farms, will also depend on various factors. Finally, marketing options and subsidies will have major impacts on future aquaculture profitability. Based on the framework of four socio-political scenarios developed in the EU H2020 project climate change and European aquatic resources (CERES), we defined how these key factors for the aquaculture sector could change in the future. We then apply these scenarios to make projections of how climate change and societal and economic trends influence the mid-century (2050) profitability of European aquaculture. We used an established benchmarking approach to contrast present-day and future economic performance of “typical farms” in selected European production regions under each of the scenarios termed “World Markets,” “National Enterprise,” “Global Sustainability” and “Local Stewardship.” These scenarios were based partly on the IPCC Special Report on Emissions Scenarios framework and their representative concentration pathways (RCPs) and the widely used shared socio-economic pathways (SSPs). Together, these scenarios contrast local versus international emphasis on decision making, more versus less severe environmental change, and different consequences for producers due to future commodity prices, cash returns, and costs. The mid-century profitability of the typical farms was most sensitive to the future development of feed costs, price trends of returns, and marketing options as opposed to the direct effect of climate-driven changes in the environment. These results can inform adaptation planning by the European aquaculture sector. Moreover, applying consistent scenarios including societal and economic dimensions, facilitates regional to global comparisons of adaptation advice both within and across Blue Growth sectors.

Evaluating the Future Efficiency of Wave Energy Converters along the NW Coast of the Iberian Peninsula

The efficiency of wave energy converters (WECs) is generally evaluated in terms of historical wave conditions that do not necessarily represent the conditions that those devices will encounter when put into operation. The main objective of the study is to assess the historical and near future efficiency and energy cost of two WECs (Aqua Buoy and Pelamis). A SWAN model was used to downscale the wave parameters along the NW coast of the Iberian Peninsula both for a historical period (1979–2005) and the near future (2026–2045) under the RCP 8.5 greenhouse scenario. The past and future efficiency of both WECs were computed in terms of two parameters that capture the relationship between sea states and the WEC power matrices: the load factor and the capture width. The wave power resource and the electric power capacity of both the WECs will decrease in the near future. The load factor for Aqua Buoy will decrease in the entire area, while it will remain unchanged for Pelamis in most of the area, except north of 43.5° N. The capture width and cost of energy will increase for both devices. The methodology here applied can be easily applied to any device and coastal domain under different climate change scenarios.

Measurement and Verification for Energy Retrofit of an Aged Office Building : Lessons Learned and Suggestions from a Perspective of Facility Manager

Purpose: Concerning the scope and method of building energy retrofit, facility manager needs to consider the energy conservation measures that can enhance clients’ productivity and expand clients’ asset value as well as building energy performance and economic feasibility. However, client’s execution permission may not be easily obtained due to expensive professional energy audit. Since facility manager knows well about the building condition, if facility manager does a preliminary measurement and verification prior to professional energy audit, it would help facility manager resolve issues proactively, such that would help facility manager provide the data and reasoning to persuade owner and clients. Eventually it would lower cost and save time for professional energy audit and energy retrofit afterward. Method: This study identifies the current status of energy flow and use in facilities for requesting professional energy diagnostic, and lists the foundational data types and collection methods to request a diagnosis order to the owner, thereby proposes a preliminary diagnostics process, which can be conducted by the facility manager independently. Result: This study provides a case study for an actual aged office building to demonstrate how the advantages and disadvantages of energy conservation measures can be analyzed and selected from the facility manager’s viewpoint and how to consult and persuade the owner and derive a building management measure that can minimize future energy costs, because the facility manager determines the scope and duration of the energy diagnosis and loss costs due to reduction in the efficiency of client’s work is calculated and consulted with the owner.

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

AbstractEnergy is an important factor in planning for ground robots, particularly in off‐road environments where the terrain significantly impacts energy usage. Unfortunately, energy costs in these environments are variable and uncertain, making planning difficult. In this paper, we present a method, based on Gaussian process regression (GPR) and known vehicle modeling information, for predicting future path energy costs. The method uses data, collected by a robot operating in a 3D environment with varying terrains, to build a map from inputs (including position, heading, slope, and satellite imagery) to power consumption. The energy costs of future paths are predicted through the summation of power predictions. Importantly, correlations in those predictions are considered to avoid overconfidence. Experimental cross‐validation results demonstrate improved accuracy of path energy cost predictions against a baseline approach, as well as the effect of Gaussian process inputs and kernel choice. Additionally, we show how vehicle modeling can aid in predicting energy costs, particularly when data on the environment is sparse.

Simulation of a controlled water heating system with demand response remunerated on imbalance market pricing

Buildings are responsible for 40% of our worldwide energy consumption and 50% of this energy is converted for Heating Ventilation Air Conditioning (HVAC) systems in buildings. The increasing share of renewable energy production required to make the transition towards a carbon neutral energy system challenges the stability of the grid. Through demand response it becomes possible to activate these systems in support of grid balancing. However, this flexibility is currently not rewarded in the market. We simulate a domestic water heater participating in the balancing of the electricity net and calculated the revenue from this action. We simulate a water heater in connection with an Economic Model Predictive Controller (EMPC) which takes future usage, energy cost, and reward for delivering balanced power into account. We show that the choice of an EMPC controller is valid as it allows the setpoint to change if certain conditions are met, leading to a more optimal revenue stream from selling flexibility. We find that the economic benefits of participating in delivering balancing power is considerable and offset an increase in energy costs. The increase in energy consumption could be justified as the participation in net stabilisation allows the macro-system to integrate more renewable energy sources. More importantly, the simulations also show that the poorer the energy performance of the water heater, the more flexibility can be sold. From a policy point of view, a minimal energy performance should be determined before allowing participation in net stabilisation.

Ions transport modelling based on Nernst-Planck theory for a novel electrochemically switched ion permselectivity system

Electrochemically switched ion permselective (ESIP) is an emerging technology which requires very little energy for trace concentration ionic separation and has markedly high selectivity. Fully understanding transport process of ions through ESIP membranes is of importance for ESIP. In this work, a two-dimensional, steady-state, simple mathematical model was established, based on Nernst–Planck theory for the ions transport in ESIP system. The model is used to evaluate and analyze the effects of inlet concentration, volume flow, and cell voltage on the separation flux. Furthermore, the current efficiency model was established, paving the way for future energy calculations and cost estimation. It is critical to minimize its operating costs and energy consumption. The simulation results are in good agreement with dynamic experiments using laboratory reactors results, which verifies the reliability and practicability of the model.

Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles.

Energy optimization for plug-in hybrid electric vehicles (PHEVs) is a challenging problem due to the system complexity and many physical and operational constraints in PHEVs. In this paper, we present a Q-learning-based in-vehicle learning system that is free of physical models and can robustly converge to an optimal energy control solution. The proposed machine learning algorithms combine neuro-dynamic programming (NDP) with future trip information to effectively estimate the expected future energy cost (expected cost-to-go) for a given vehicle state and control actions. The convergences of these learning algorithms were demonstrated on both fixed and randomly selected drive cycles. Based on the characteristics of these learning algorithms, we propose a two-stage deployment solution for PHEV power management applications. Furthermore, we introduce a new initialization strategy, which combines the optimal learning with a properly selected penalty function. This initialization scheme can reduce the learning convergence time by 70%, which is a significant improvement for in-vehicle implementation efficiency. Finally, we develop a neural network (NN) for predicting battery state-of-charge (SoC), rendering the proposed power management controller completely free of physical models.

Interference of billing and scheduling strategies for energy and cost savings in modern data centers

The high energy consumption of HPC systems is an obstacle for ever-growing systems. Unfortunately, energy consumption does not decrease linearly with reduced workload; therefore, energy conservation techniques have been deployed on various levels which steer the overall system. While the overall saving of energy is useful, the price of energy is not necessarily proportional to the consumption. Particularly with renewable energies, there are occasions in which the price is significantly lower. The potential of saving energy costs when using smart contracts with energy providers is lacking research. In this paper, we conduct an analysis of the potential savings when applying cost-aware schedulers to data center workloads while considering power contracts that allow for dynamic (hourly) pricing.The contributions of this paper are twofold: (1) the theoretic assessment of cost savings; (2) the development of a simulator to replay batch scheduler traces which supports flexible energy cost models and various cost-aware scheduling algorithms. This allows to approximate the energy costs savings of data centers for various scenarios including off-peak and hourly budgeted energy prices as provided by the energy spot market. An evaluation is conducted with four annual job traces from the German Climate Computing Center (DKRZ) and Leibniz Supercomputing Centre (LRZ).The theoretic analysis indicates a cost savings for 4–8% when shutting down unused client nodes, and 6–20% with hourly cost models and optimal scheduling. The experimental validation of a practicable scheduler increases the accuracy against the theoretical best case analysis. As expected, a cost-efficient scheduling algorithm that is fed with the information about future energy costs shifts the jobs to the timeslots where the job execution is cheaper and reduces the energy expenditure, yet increases the waiting times of pending jobs. However, the expected savings for this effort are not justifiable compared to the simple strategy of turning off the unused nodes. Additionally, we compare the cost savings to the total costs of ownership showing that smaller systems with on-demand provisioning yield better cost efficiency.

New modeling formalism for the energy simulation of conveyor systems

Presented in this paper is a modeling formalism for the energy simulation of conveyor systems. Reducing the energy consumption of a conveyor system can decrease a significant portion of the operation cost, contribute to the sustainable operation, and prepare for future energy cost increases related to regulations for green industry. Therefore, the energy consumption is becoming one of the key performance indicators in conveyor systems design. To construct a simulation model considering the energy consumption of conveyor systems, the modeling formalism is necessary to represent the multiple material transportations and conveyor operation switching (on and off) depending on material load states. This paper proposes a new modeling formalism satisfying the requirements by extending the conventional discrete event system specification (DEVS) formalism. In the proposed formalism, each simulation entity has an energy model, which is derived from the reverse engineering of real conveyor operations, to determine energy consumption states and rates. The simulation results of this research have been applied to the revision of a warehouse conveyor system in Korea.