Demand Growth Scenarios Research Articles

Evaluation of surface water supply impacts from permit exemptions: A comparison with climate change and demand growth

AbstractMany states in the Eastern U.S. have limited water withdrawal regulations, posing significant risks to water supply management during periods of low flows. While these states require water withdrawal permits, exemptions for grandfathered withdrawals that allow unregulated access to surface water are common. Such permit exemptions present a challenge to water supply management, as full utilization of allowable withdrawals by permit‐exempt users could pose risks to maintaining adequate water supplies for current and projected demand. This study used reported permit exemption data in Virginia to understand the extent, volume, and potential impact of permit‐exempt withdrawals on 30‐ and 90‐day low flows. The permit‐exempt withdrawal values used in this study were obtained from Virginia Department of Environmental Quality. Maximum permit‐exempt withdrawal volumes were significantly higher than projected future demands in permitted users. The impacts of these withdrawals on drought flows were compared with the impacts presented by climate change and demand growth. Widespread reduction in flows was observed with the “dry” future climate change scenario, while impacts were more localized in the exempt users and the demand growth scenarios. The impacts of exempt users exceeded the impact of climate change and demand growth scenarios in many regions during low‐flow periods. Therefore, more comprehensive water planning, policy and research is needed to address the impact of permit exemptions.

Long-term optimal capacity expansion planning for an operating off-grid PV mini-grid in rural Africa under different demand evolution scenarios

Using real-time load data and HOMER Pro's ‘multi-year’ optimization tool, this paper investigates the long-term cost optimal capacity expansion planning (CEP) for an overloaded photovoltaic (PV) mini-grid (MG) with storage batteries in off-grid rural Ethiopia over a 20-year planning horizon. Three distinct annual energy demand growth scenarios were considered: 0 % (fulfils the minimum load requirement), 5 %, and a 15 % from productive users only. In all scenarios, the generation mix consists of only solar energy and the maximum allowable capacity shortage (MACS) is limited to 10 %. The findings reveal that, in all scenarios, the largest capacity expansion is performed on the battery and PV systems, covering up to 73 % and 35 % of the total expansion costs, respectively. The annual unmet load fraction of the expanded MG system ranges from 5.9 % in scenario-3 to 9.4 % in scenario-1, and the cost of electricity (LCOE) ranges from $0.404/kWh in scenario-3 to $0.887/kWh in scenario-1. The results indicate that the scenario-3 expansion path is comparatively cost-effective and has the highest reliability; but it still falls short of fully satisfying the required load demand and is not financially viable. Surprisingly, increasing the reliability of the scenario-3 capacity expansion from 94 % to 100 % raises the MG's Net Present Cost by 37 %. The sensitivity analysis shows that the MACS, ambient temperature, and battery's depth of discharge significantly affect the cost and performance of the capacity expansion. The study demonstrates (a) there are significant trade-offs between minimizing MG expansion costs and maximizing reliability levels; (b) capacity expansion based solely on cost-minimization without considering key constraints and uncertainties (demand, cost, PV, and battery degradations) may not provide a practical and robust solution to severe reliability issues, (c) capacity expansion that supports demand from productive users increases the cost-effectiveness and bankability of isolated MGs.

Perspectives on peak demand: How is ERCOT peak electric load evolving in the context of changing weather and heating electrification?

This analysis characterizes the past and future evolution of peak electricity demand in the Electric Reliability Council of Texas (ERCOT) service area and assesses how these trends may be driven by changing weather and heating electrification. To accomplish this, historical ERCOT demand data from 1997 to 2021 were utilized along with weather data for the days in which peak demand occurred and from a future climate scenario. The results inform resource planners on the relationship between peak heating and cooling load and ambient temperature and present peak demand growth scenarios for 2025 through 2050. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. These phenomena are likely consequences of temperatures that were fairly constant on summer peak demand days, but varied widely on winter peak demand days. The erratic nature of winter peak demand is also likely caused by the fact that electrical heating equipment becomes increasingly inefficient at lower temperatures, as is demonstrated by the polynomial relationship identified between per capita winter peak demand and heating degree days. Additionally, historical winter peak demand was shown to be growing more quickly than summer peak demand. This phenomenon is likely the result of increases in electrical efficiency of cooling and increases in electricity consumption that result from the rising penetration of electrical heating equipment that replace gas furnaces. Future peak demand scenarios indicate that winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted for resource planners. These findings might foreshadow future resiliency challenges that other regions will face as electric heating equipment is deployed in place of boilers or furnaces for decarbonization purposes.

Sizing solar-based mini-grids for growing electricity demand: Insights from rural India

Abstract Mini-grids are a critical way to meet electricity access goals according to current and projected electricity demand of communities and so appropriately sizing them is essential to ensure their financial viability. However, estimation of demand for communities awaiting electricity access is uncertain and growth in demand along with the associated cost implications is rarely considered during estimation of mini-grid sizing. Using a case study of two rural communities in India, we assess the implications of demand growth on financial costs and performance of a mini-grid system consisting of solar photovoltaic (PV) panels and battery storage using two different system sizing approaches. We show a cost-saving potential of up to 12% when mini-grids are sized using a multi-stage approach where mini-grids gradually expand in several stages, rather than a single-stage optimisation approach. We perform a sensitivity analysis of the cost of the two sizing approaches by varying six key parameters: demand growth rate, logistics cost, system re-sizing frequency, likelihood of blackouts, solar PV and battery cost, and degradation rate. Of these, we find that system costs are most sensitive to variations in demand growth rates and cost decreases in solar PV and batteries. Our study shows that demand growth scenarios and choice of mini-grid sizing approaches have important financial and operational implications for the design of systems for rural electrification.

Impact of India's power purchase agreements on electricity sector decarbonization

A large number of Indian thermal power plants are tied under power purchase agreements with distribution companies. However, the existing energy models do not account for these ‘take or pay’ contracts for investigating climate policies. In this context, this paper investigates the impact of these power purchase agreements on power sector operations, cost of electricity and emissions from the Indian power sector. An optimization model was developed for this study with 620 coal, 239 gas, 21 nuclear and 686 hydro units with technology and operational characteristics to analyze electricity dispatch for power sector. The operations of thermal power plants were analyzed with and without power purchase agreements, and low carbon policy scenario. The scenarios are compared for power plant operations, cost of electricity, stranded assets, and emissions. The results show that there is a significant cost advantage for plants covered under power purchase agreements which favors the operations of older inefficient power plants in baseline scenarios. In addition, the representation of power purchase agreements raises the cost of electricity by 26 percent. A change in operating strategy from least cost to least emissions with a lower demand growth scenario can mitigate 24 percent emissions. The cost of carbon abatement is 268–277 USD/tonne for policy scenarios. Options like renegotiation of power purchase agreements can be considered to mitigate emissions.

COVID-19 and the Global Mining Industry

Abstract The world is currently experiencing a rapid and deep economic slowdown as a result of COVID-19 mitigation efforts. The depth and global nature of this recession, which could turn into a depression, suggests that this pandemic will significantly affect the demand for metals and the global mining sector. The majority of governments consider mining to be essential, meaning that the effect of mitigation on the mining industry and on metal production has been minimal to date. However, increases in metal stocks and decreases in metal prices suggest that the mining industry will be negatively affected by the COVID-19 crisis, at least in the short term. This paper presents an overview of the effects of COVID-19 mitigation on the mining sector to date. That includes variations in metal and commodity prices and stocks during the crisis and the outlining of two possible scenarios for COVID-19 related impacts. The first involves persistent supply-chain disruptions, where metal supply is restricted by logistical or COVID-19–related mitigation impacts on intermediates such as smelters and refiners. This restriction of supply could cause higher metal prices but also could cause issues with demand for ores and concentrates that negatively affect individual mining operations. More likely is a second slower demand growth scenario in which a global decrease in demand for metals causes further lowering of metal prices with associated negative economic impacts on mining operations. However, further research into global metal supply chains and the impact of the COVID-19 crisis on individual metals is needed. Key remaining unknowns include the influence of mitigation efforts on global metal supply and demand, the effect of these efforts on metal prices, and the geography of supply chains.

Vision 2023: Scrutinizing achievability of Turkey's electricity capacity targets and generating scenario based nationwide electricity demand forecasts

In the last couple of years, Turkey faced serious economic and geopolitical problems. As a result, many from the academia and industry now have suspicions about the viability of Turkey's Vision 2023 electricity targets. Consequently, in this study an up to date forecast of Turkey's electricity demand till 2023 is generated and achievability of the Vision 2023 electricity capacity targets are scrutinized. Turkey's electricity demand till 2023 is forecasted using the semi-empirical electricity demand model generated in this study. Different average % annual electricity demand per capita growth scenarios are used for the electricity demand modelling. Results from the modelling study showed that Turkey's electricity demand in 2023 could be 327,000 GWh if the very low demand scenario occurs or 362,000 GWh if the low demand growth scenario prevails or 386,000 GWh if the medium demand growth scenario succeeds or 429,000 GWh if the high demand growth scenario happens. The demand forecasts generated in this study match 94.3% (or higher) with previous forecasts found from the literature. The scrutiny showed that wind energy installed capacity in 2023 (estimated at nearly 11,000 MW) might be significantly lower than the official target of 20,000 MW. It is also pinpointed that one nuclear power plant might become operational (25% operational – one out of four units/reactors) instead of the planned two in 2023. Putting these potential target deviations aside, the scrutiny showed that Turkey's installed capacity in 2023 would most probably be greater than 110,000 MW and might in fact reach to the planned 120,000 MW. This is an important finding for policy analysts and researchers. Finally, it is suggested that the work on Turkey's energy projects must be accelerated and some of the Vision 2023 installed capacity targets (wind and nuclear) should be revised by the authorities.

Simulation of demand growth scenarios in the Colombian electricity market: An integration of system dynamics and dynamic systems

Modeling and simulation of electricity markets have increasingly involved the use of a system dynamics (SD) approach. Accordingly, the resulting dynamic hypothesis and the stock-flow structures are represented and simulated using softwares such as Stella, Powersim, iThink, or Vensim. However, SD models can be exploited even more, of which the investigation of signals in the time domain or the sensitivity analysis is just a small part of the study. Since SD models are mathematical objects, they deserve an analytical or numerical study using tools provided by the dynamic systems (DS) methodology. Therefore, this paper not only studies the dynamic hypothesis or the stock-flow structure of an electricity market model in the classical form, but also uses its inner mathematical object to provide a deeper insight into the system. Using MATLAB/Simulink®, the system is evaluated from a different approach not yet reported in the literature. The combination of the SD and DS methodologies can open the door to a new and alternative method of analysis for electricity market models and even for any SD model. In fact, this paper demonstrates that with this methodologies combination, more detailed analysis strategies and novel insights of the SD models can be developed, which can be easily exploited by policy makers to suggest improvements in regulations or market structures. Moreover, considering that the energy market is evolving, and a series of macro and microstructural changes are impacting demand, we consider as an example a simplified version of the Colombian electricity market to report a detailed description of its dynamics under a broad range of growth rate of demand (GRD) scenarios. Our study, inspired by the bifurcation and control theory of DS, primarily shows that Colombia is in dire need of a new capacity before 2020 to avoid rationing events expected to occur in the upcoming years.

Static transmission expansion planning for realistic networks in Egypt

Transmission network planning (TEP) is very important task in electric power systems. It begins with the establishment of power demand growth scenarios, in accordance with forecasts along the time and to obtain the optimal expansion plan, while fulfilling power systems constraints. This paper proposes a two-stage TEP procedure for two realistic transmission Egyptian networks, Western Delta Network (WDN) and 500kV of Extra High Voltage Network (EHVN). In the first stage, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to obtain the predicted long-term load forecasting (LTLF) up to 2030. In the second one, the Integer Based Particle Swarm Optimization (IBPSO) technique is developed for solving the static TEP problem. The proposed TEP aims at finding the optimal transmission routes, at least capital investment costs, to meet the forecasted load. The TEP problem is formulated as non-linear, large scale, mixed-integer and non-convex optimization problem. The static TEP problem is employed using DC power flow model. The proposed TEP methodology is tested on standard Garver 6-bus test systems. The load forecasting methodology is dependent on the historical (Actual) peak load data for the UEN from 1993 to 2015. The location and capacity of new site of generation station are selected to meet the demand required. Also, the AC load flow method is emerged with the TEP solution employed by IBPSO for Garver and EHVN to assess the voltage, reactive power and security constraints. Numerical results show the capability of the proposed procedure to solve TEP problem at acceptable economical and technical benefits.

Uncertainty in the UKKs National Infrastructure Assessment of Mobile Telecommunication Infrastructure: From LTE to 5G and Beyond

The UK’s National Infrastructure Commission is undertaking its first ever National Infrastructure Assessment, of which telecommunications is a key component. The aim of this task is to ensure efficient and effective digital infrastructure delivery over the long term, the results of which will be used to direct both industry and government over coming decades. However, taking a strategic long-term approach to the assessment of telecommunication infrastructure is a challenging endeavour due to rapid technological innovation in both the supply of, and demand for, digital services. In this paper, the uncertainty associated with the National Infrastructure Assessment of telecommunication infrastructure is explored, focusing specifically on issues pertaining to uncertainty in future demand. We not only quantify the uncertainty of different future demand scenarios, but also measures the performance of different infrastructure interventions. This is vital information for policy makers to understand the long-term relationship between available capacity and future demand. The methodology applies the Cambridge Communications Assessment Model, which has been developed exclusively for the evaluation of national digital infrastructure strategies, over 2017-2030. We focus not only on future data demand growth, but also on using long-term demographic forecasts that capture changes in fertility, mortality and migration. We test the impact of different infrastructure intervention strategies by developing novel infrastructure deployment algorithms which represent operator behaviour. Consequently, four key intervention options are explored for different demand scenarios including (i) minimum intervention, (ii) spectrum integration on brownfield macrocell sites, (iii) greenfield small-cell deployment or (iv) a hybrid approach using both spectrum and small cells. We find that a minimum intervention approach performs poorly against future demand growth, while spectrum-based strategies only meet future needs in a low demand growth scenario (which is unlikely). The best performing strategies across a range of scenarios include deploying either a small cell layer (operating at 3700 MHz), or a combination of a small cell layer and the simultaneous integration of available spectrum resources on brownfield macrocell sites (700, 800, 2600, 3500 MHz). No capacity deficit occurred for any of these interventions across the demand scenarios. However, this additional capacity comes with a much larger cost (at a time when revenue is static or decreasing in the mobile industry), leading to a trade-off between available capacity and efficiency of capital deployed. The results conclude that while national infrastructure assessment is a valid method for thinking more strategically about our long-term infrastructure needs, we must recognise the inherent uncertainty associated with this particular sector. Rapid technological innovation affects our ability to accurately forecast long-term roll-out, making it essential that rigorous examination of this uncertainty is both quantified and visualised to support policy decision-making. To aid in this endeavour, the Cambridge Communications Assessment Model has been developed in accordance with open-source principles, with the model code being freely distributed.

Values of decentralized systems that avoid investments in idle capacity within the wastewater sector: A theoretical justification

In this work, the values of decentralized (onsite) systems that avoid investments in idle capacity within wastewater plans are quantitatively justified using the specific net present value (SNPV) approach. SNPV is a currently proposed criterion in environmental engineering economics that is defined as the net present value of the cost per unit of service or per population equivalent (PE). The SNPV approach was reintroduced with bugs fixed and then applied to the economic analysis of the capital and operating costs of one-stage completed central plants, stage-expanded central plants, and decentralized treatment facilities. The results show that under a demand growth scenario, the central plant will inevitably reach idle capacity, which can be reduced by a staged expansion. However, the staged expansion plan will lose the economies of scale and, hence, is only viable under projections of a low or moderate price inflation rate or high demand growth rate. Onsite treatment systems can theoretically achieve 100% utilization. Assuming that the capital costs per PE of the onsite and central systems are equal, the former is economically favorable in most cases of price inflation as a result of its cost saving on idle capacity. Onsite treatment systems can be viable even though their capital expenditures per PE are higher than that of a comparable centralized option as to a capital investment. This finding suggests wide opening of onsite technology choices. Use of the SNPV showed that average operating expenses of centralized plants decrease as demand growth rates increase as a benefit of economies of scale, whereas those of onsite treatment systems depend only on price inflation. Semi-decentralized systems feature both the financial advantage of the onsite system (capital investment) and the superiority of centralized systems (operation and maintenance); thus, it is worth consideration. The results of this study illustrate not only the value of decentralized systems but also the value of the SNPV approach in the planning of wastewater services, especially in areas undergoing high demand growth.

The Impact Of Electrical Co-Generation On The Oklahoma Economy

Using input-output analysis, this paper estimates the impact of electricity co-generation on the Oklahoma economy, assuming alternative electricity growth rates and co-generation capacity. The impact is affected by co-generation operation, utility operation, co-generation construction, utility construction, and electricity rate changes. This study indicates that co-generation can have a beneficial impact on output, earnings, employment and taxes with a rapid electricity demand growth scenario and a detrimental impact with slower growth.

Scenario-Based Forecast of Regional Water Demands in Northeastern Illinois

This paper reviews the principles of scenario-based planning and illustrates an application of this approach in a study of a regional water supply and demand in northeastern Illinois. The scenario-based planning has been adopted over the traditional approaches because the scenario approach facilitates an explicit consideration of uncertainty in both supply and demand. An important component of the study was the development of scenario-based forecasts of water demand for major user sectors and geographical subareas within the 11-county region which, includes the city of Chicago and 250 municipalities. The study revealed that the increase in total withdrawals in 2050, under baseline scenario, would be 530 mgd (millions of gallons per day) (36 %) and could reach as high as 949 mgd (64 %) under high demand growth scenario. Thus, meeting these additional demands would require large capital outlays for water infrastructure and would likely have significant impacts on some of the regional sources of water supply...

Greek airport capacity utilisation: what about airfreight?

The paper examines capacity availability in 11 Greek airports in order to explore the possibilities of attracting airfreight traffic. Capacity is estimated based on the minimum value observed between apron and runway service ability. Secondary data are used for the estimation of capacity and traffic demand, based on the number of planes handled per hour. Utilisation levels are estimated on today's capacity and traffic figures. A projection based on different demand growth scenarios is also made for the year 2030. Results show that most Greek airports have significant capacity available, which stays idle for most of the 18-hour time frame used here. Runway appears to be the capacity factor that constrains airport operations more. Among the factors that decision-makers should consider in attracting new airfreight demand is the examination of the pricing policies followed, the competitiveness levels of airports and the necessary infrastructure required for the handling of this type of demand.

Delayed Carbon Policy Certainty and Electricity Prices in Australia*

The power industry has been grappling with regulatory uncertainty in relation to carbon since late 2004 when Australian state governments committed to the introduction of an emissions trading scheme. This article estimates the additional cost to electricity users associated with the sub‐optimal introduction of new power generating capacity given regulatory delays. We find the costs to be significant; under a business‐as‐usual electricity demand growth scenario, prices in 2020 would be about $8.60/MWh higher than necessary. We also find that costs to consumers are lower where complementary policies are introduced to encourage energy efficiency and renewable energy.

An assessment of the potential benefits from integrated electricity capacity planning in the northern Middle East region

Demand for electricity in the northern Middle East region has experienced considerable growth in the past few decades. However, power sector planning in the countries of this region has been mainly carried out in an independent manner. Electricity trade has been discussed as a means for regional cooperation and a viable option for integrated regional planning. This paper reports on a study to evaluate the potential economic benefits from such an integrated planning approach. A mathematical model is formulated and used with country level technical and economic data with different acceptable levels of trade and for different demand growth scenarios. This demonstrates potential gains from complementarities of peak load and diversity of fuel resources in the studied region. Results obtained for a northern Middle East region consisting of Egypt, Iraq, Jordan, Lebanon and Syria estimate savings of more than 13% in required installed generation capacity in these countries. The savings occur over a 15-year planning horizon when an integrated regional planning approach is followed allowing individual countries to have up to 50% of their peak demand met by other countries in the region. The analytical model developed and applied here can prove to be a valuable tool to decision makers in the region as they study future plans for the energy sector and negotiate their participation in regional interconnection projects.

Generation planning including distributed generators under uncertainty of demand growth

AbstractThis paper addresses the problem of generation planning in the competitive power market with uncertainty of demand growth. The distributed generator (DG) is given attention against a large‐scale generator to correspond to uncertain demand growth. Optimization consists of minimizing the average cost and hedging risk over the scenario trees of demand growth. At first, based on the idea of a Real Option, Dynamic Programming using the utility function is applied to generation planning. Utility functions can model investor's risk‐return profile. The decisions in the first stage indicate that they are influenced by the type of utility functions and demand growth scenarios, and data of generators. Next, a Monte Carlo simulation is applied to a Brownian motion model of demand growth. This model can increase the possible number of demand levels. With this simulation, the case that the distributed generator has an advantage over the large‐scale generator is quantitatively discussed. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 146(3): 17–25, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10276

Generation Planning including Distributed Generator under Uncertainty of Demand Growth.

This paper addresses the problem of generation planning in the competitive power market with uncertainty of demand growth. The distributed generator (DG) is paid attention against a large-scale generator to correspond uncertain demand growth. Optimization consists in minimizing the average cost and hedging risk over the scenario trees of demand growth. At first, based on the idea of Real Option, Dynamic Programming using the utility function is applied to generation planning. Utility functions can model investor's risk-return profile. The decisions in the first stage indicate that they are influenced by the type of utility functions and demand growth scenarios, and data of generators. Next, Monte Carlo Simulation is applied to Brown motion model of demand growth. This model can increase the possible number of demand level. With this simulation, the case that the distributed generator has advantage against the large-scale generator is quantitatively discussed.

Technology Dynamics and Greenhouse Gas Emissions Mitigation: A Cost Assessment

This article analyzes long-term greenhouse gas (GHG) emissions and their mitigation in a family of high economic and energy demand growth scenarios in which technological change unfolds in alternative “path dependent” directions. Four variants of this family are developed and used as baseline scenarios, for which alternative policy cases leading to a stabilization of atmospheric CO 2 concentrations at 450, 550, 650, and 750 parts per million by volume (ppmv) by the end of the 21st century are examined. The baseline scenarios share common demographic, economic, and energy demand developments, but explore alternative development pathways of technological change and resource availability. We illustrate the sensitivity of projected future GHG emission levels and resulting global climate change to alternative developments in energy systems technologies. We conclude that uncertainties in technological change are as important for determining future GHG emissions as uncertainties in long-term demographic and economic developments. We also illustrate that differences in costs between alternative baseline scenarios of technological change may be larger than the cost differences of reaching alternative environmental (climate change stabilization) targets. Under our assumptions of high economic and energy demand growth, even in scenarios favoring fossil fuels, the long-term technology portfolio needs to include improvements in zero-carbon technologies and gas-related technologies and infrastructures. We suggest that improvements in these technology options are a robust hedging strategy for an uncertain energy future.

Can China Feed its People into the Next Millennium? Projections for China's grain supply and demand to 2010

Econometric models of grain supply and demand which incorporate the effects of policy interventions and institutional changes are used as the basis for projecting grain balances in China to 2010. The paper outlines key assumptions about future changes in grain policies and other important explanatory variables in the models. On the supply side, projections are made for grain sown area, grain yield and total grain output. Future grain demand is disaggregated into four categories, human food, animal feed, industrial uses and seed grain, each incorporating high, medium and low demand growth scenarios. A grain balance sheet is constructed for 2010 which shows a ‘most probable’ import requirement of about 32 million tones—a 10 million tones increase over the actual 1995 level. Thus, net grain imports will increase substantially but China is unlikely to become an importer on a massive scale.