Data-Driven Competitor-Aware Positioning in On-Demand Vehicle Rental Networks

We study a novel operational problem that considers vehicle positioning in on-demand rental networks, such as car sharing in the wider context of a competitive market in which users select vehicles based on access. Existing approaches consider networks in isolation; our competitor-aware model takes supply situations of competing networks into account. We combine online machine learning to predict market-level demand and supply with dynamic mixed integer nonlinear programming. For evaluation, we use discrete event simulation based on real-world data from Car2Go and DriveNow. Our model outperforms conventional models that consider the fleet in isolation by a factor of two in terms of profit improvements. In the case we study, the highest theoretical profit improvements of 7.5% are achieved with a dynamic model. Operators of on-demand rental networks can use our model under existing market conditions to build a profitable competitive advantage by optimizing access for consumers without the need for fleet expansion. Model effectiveness increases further in realistic scenarios of fleet expansion and demand growth. Our model accommodates rising demand, defends against competitors’ fleet expansion, and enhances the profitability of own fleet expansions.

IMPACT OF INTEREST RATE, INFLATION AND GROSS DOMESTIC PRODUCT TO DEMAND FOR PROPERTY CREDIT IN INDONESIA

An increase in credit, especially consumption credit, can trigger aggregate demand growth above potential output which causes the economy to heat up. This study aims to analyze the effect of macroeconomic variables, such as interest rates, inflation, and gross domestic product (GDP), on the demand for property credit in Indonesia with the period January 2011 – December 2018. The results show that in the short term, the interest rate lag 1 and lag 2, inflation lag 1, and GDP significantly influence the demand for peoperty credit. While, in the long term, only the interest rates and GDP significantly influence to the demand for property credit.Keywords: Property Credit, Interest Rates, Inflation, GDP, ARDL JEL: C22, E51, G21

The Long-Term Impact of the Electorate on the Swiss Electricity Market Transition

The Swiss government, through its Energy Strategy 2050, is engaged on a path to transition Switzerland to become a carbon-neutral country by the year 2050. In this chapter, we look at the impact that the electorate can have on this transition and on the Swiss electricity market. This is done using hybrid agent-based modelling. We model the Swiss electricity market and we add to this a model of the policy-making process. This allows us to study which policy instruments are more likely to be implemented depending on the Swiss electricity market progression and on the policy actors’ interests. The results have shown that the electorate has a limited impact on the policy chosen and on the electricity market. Overall, an environmentally conscious electorate leads policy actors to select the carbon tax as a policy more often. This, however, has the adverse effect to increase the electricity price and increase import dependency in winter. In high demand growth scenarios, the carbon tax policy is not sufficient to stem the construction of gas turbine power plants. We also show that because the electricity model does not consider an extended demand response option or technology advancement, the knowledge gained from this model is limited. This drives the behaviour of the model into scenarios which are unlikely to happen, such as a large increase of the gas turbine power plants. Overall, we conclude that, in their current form, even with an environmentally conscious electorate, the electricity market conditions do not allow Switzerland to reach its emissions targets.

The oil market will move towards balance in 2022

Significance The rebound in demand from the pandemic slump should keep the market in deficit, supporting current price levels. However, in 2022, demand growth is expected to slow, while supply from non-OPEC+ producers will accelerate. A potential return of Iranian oil exports and further pandemic reversals remain major downside risks. Impacts Forecasts suggest a return to normality in 2022, but discontinuities with the past remain powerful forces reshaping global energy markets. If pandemic concerns recede, energy transition impacts will move to the fore. Non-OPEC supply growth plus expectations of more muted demand increases mean OPEC will still have a 'free rider' problem to address. Oil services companies’ prospects should improve but margins will remain thin.

Comments: Oil Demand Optimism

Forecasts for oil demand are looking up, according to OPEC and the International Energy Agency as of mid-July. Will the optimistic views prove to be on target? We have learned how the market can shift or wildly careen, both historically and in the very recent past. Looking at the forecasts, which reflect a consensus of sorts, is encouraging for producers. OPEC’s monthly report of 15 July projected global oil demand to reach nearly 100 million B/D next year, a level similar to pre-pandemic in 2019. The 2021 oil demand growth remains unchanged at 5.95 million B/D, or approximately 6.6%. Led by demand growth in the US, China, and India, a 3.4% increase is expected in 2022 to 99.86 million B/D and would average more than 100 million B/D in the second half of the year. “Solid expectations exist for global economic growth in 2022,” OPEC said. “These include improved containment of COVID-19, particularly in emerging and developing countries, which are forecast to spur oil demand to reach pre-pandemic levels in 2022.” If the actual recovery tracks with these predictions, OPEC can dial back further its record-level supply cuts made in 2020. The IEA points to the growth expected in global electricity demand as spurring fossil-fuel demand, including oil, coal, and natural gas. After falling by around 1% in 2020, electricity demand growth may approach 5% in 2021 and 4% in 2022. The Asia Pacific region will account for the majority of the increases. China, the world’s largest consumer of electricity, leads the tally, accounting for more than 50% of the 2022 growth. India, the third largest, will account for 9% of the global electricity growth. Renewables are expected to be able to serve around half of the projected growth in global demand in 2021 and 2022. IEA wrote, “Renewable electricity generation continues to grow strongly—but cannot keep up with increasing demand. After expanding by 7% in 2020, electricity generation from renewables is forecast to increase by 8% in 2021 and by more than 6% in 2022.” Fossil fuel-based electricity is set to cover 45% of additional demand in 2021 and 40% in 2022. After declining by 4.6% in 2020, coal-fired electricity generation will increase by nearly 5% in 2021, exceeding pre-pandemic levels. In 2022, it will grow another 3% and could reach an all-time high. Natural gas-generated electricity lags coal because it is less commonly used in the Asia Pacific and competes with renewables in the US and Europe. It is expected to increase globally by 1% in 2021 and by nearly 2% in 2022 after declining by 2% in 2020. The US Energy Information Administration published a global financial review last month of 91 oil and gas companies, most headquartered in the US, in the first quarter 2021. It indicated that companies are implementing their plans announced over the past year to reduce capital expenditures to pay down debt. Capital expenditure in 1Q2021 was reported as $48 billion, 28% lower than in 1Q2020 and the second- lowest amount for any quarter since 2016. Cash from operations in Q1 this year totaled $79 billion, 19% higher than in 1Q2020; about 76% of companies had positive free cash flow. Overall, the companies decreased debt by $16 billion in 1Q2021, and the long-term debt-to-equity ratio decreased to 54%.

Automatic Tariff Controller

The growth in the demand for electricity in Libya during the last decade witnessed a dramatic growth in the national's annual residential development has played a major role in boosting the demand for electric power. The domestic sector in Libya already accounts for approximately 39 percent of electricity demand. To meet the projected demand for electrical power to cope with, the development plans, increases in the population and the rising in the living standards, government will have to accomplish new power generating units. Comparing with the high budget of constructing new generating power units, load management system it would be attractive resource that should be seriously considered as an important part of national energy program, where demand growth rate exceeds the supply since it is playing an increasing role around the world as a valuable and cost effective energy resource. Hence, was light projecting on power load management program, for its benefit in reducing the energy demand at peak timeown

Optimal Cloud Orchestration Model of Containerized Task Scheduling Strategy Using Integer Linear Programming: Case Studies of IoTcloudServe@TEIN Project

As a playground for cloud computing and IoT networking environment, IoTcloudServe@TEIN has been established in the Trans-Eurasia Information Network (TEIN). In the IoTcloudServe@TEIN platform, a cloud orchestration for conducting the flow of IoT task demands is imperative for effectively improving performance. In this paper, we propose the model of optimal containerized task scheduling in cloud orchestration that maximizes the average payoff from completing tasks within the whole cloud system with different levels of cloud hierarchies. Based on integer linear programming, the model can take into account demand requirement and resource availability in terms of storage, computation, network, and splittable task granularity. To show the insights obtainable from the proposed model, the edge-core cluster of IoTcloudServe@TEIN and its peer-to-peer federated cloud scenario with OF@TEIN+ are numerically experimented and herein reported. To evaluate the model’s performance, payoff level and task completion time are considered by comparing with a well-known round-robin scheduling algorithm. The proposed ILP model can be a guideline for the cloud orchestration in IoTcloudserve@TEIN because of the lower task completion time and the higher payoff level especially upon the large demand growth, which is the major operation range of concerns in practice. Moreover, the proposed model illustrates mathematically the significance of implementing cloud architecture with refined splittable task granularity via the light-weighted container technology that has been used as the basis for IoTcloudServe@TEIN clustering design.

Scenarios of future Indian electricity demand accounting for space cooling and electric vehicle adoption

India is expected to witness rapid growth in electricity use over the next two decades. Here, we introduce a custom regression model to project electricity consumption in India over the coming decades, which includes a bottom-up estimate of electricity consumption for two major growth drivers, air conditioning, and vehicle electrification. The model projections are available at a customizable level of spatial aggregation at an hourly temporal resolution, which makes them useful as inputs to long-term electricity infrastructure planning studies. The approach is used to develop electricity consumption data sets spanning various technology adoption and growth scenarios up to the year 2050 in five-year increments. The aim of the data is to provide a range of scenarios for India’s demand growth given new technology adoption. With long-term hourly demand projections serving as an essential input for electricity infrastructure modeling, this data publication enables further work on energy efficiency, generation, and transmission expansion planning for a fast-growing and increasingly important region from a global climate mitigation perspective.