Periods Of High Prices Research Articles

A Cardinality Minimization Approach to Security-Constrained Economic Dispatch

We present a threshold-based cardinality inimization formulation for the security-constrained economic dispatch problem. The model aims to minimize the operating cost of the system while simultaneously reducing the number of lines operating in emergency operating zones during contingency events. The model allows the system operator to monitor the duration for which lines operate in emergency zones and ensure that they are within the acceptable reliability standards determined by the system operators. We develop a continuous difference-of-convex approximation of the cardinality minimization problem and a solution method to solve the problem. We also present mixed-integer programming reformulation of the cardinality minimization problem. Our numerical experiments demonstrate that the cardinality minimization approach reduces the overall system operating cost as well as avoids prolonged periods of high electricity prices during contingency events.

Grid integration of electric vehicles for optimal marginal revenue of distribution system operator in spot market

Electric vehicles (EVs) combined with low-carbon generator sets can significantly reduce CO2 emissions in the transportation sector. EVs can provide flexible auxiliary services for the grid through proper power dispatching, which benefits both EVs and the grid. This paper discusses the optimal charging and discharging plan for EVs for the purpose of a distribution system operator (DSO) while obtaining the optimal day-ahead bidding strategy for DSO in the spot market. Considering the different charging demands of EV users, two contract models of vehicle-to-grid (V2G) and smart charging services are introduced. Then, K-means clustering is adopted to manage the spatiotemporal uncertainties of EVs, in order to maximize the expected marginal revenue of DSO. Then the optimization model is proposed by considering the real conditions campus grid in Shenzhen and the electricity market in Guangdong province. The result shows that the DSO could obtain the marginal revenue not only by the optimal day-ahead bidding strategy but also by scheduling EVs’ charging and discharging. In addition, EV users could reduce the charging cost by charging their EVs during low electricity price periods and get extra revenue by exporting power to the utility grid during high price periods.

Dynamic air ticket pricing using reinforcement learning method

This paper studies a dynamic air ticket pricing problem in a strategic and myopic passengers co-existence market. The strategic or myopic passengers can be further divided into high-valuation and low-valuation groups according to how they evaluate their purchases. The strategic passengers have different strategic levels. When the airline sets a ticket price, every passenger makes his or her purchase decision according to his or her type and the strategic level, or might select “wait” or “leave (the market)”. The paper firstly proposes a dynamic pricing algorithm in which the utilities of both the airline and passengers are considered. The reinforcement learning (RL) is employed to deal with the progressive or dynamic decision-making framework, in which the dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP) and the Q-learning is adopted to solve the problem. By using this method, the airline can adaptively decide the ticket price based on passengers strategic behaviors and the time-varying demand. The effects of the passenger type proportion and strategic level are analyzed. The computational results show the higher proportion of strategic passengers is, the smaller price increase the airline can adopt, and the higher proportion of high-valuation strategic passengers is, the larger price increase the airline can put to use under the same strategic level. If the proportion of low-valuation strategic passengers is higher, the price increase should be gentle and step by step when the price increase strategy is adopted. If the airline uses price-cut policy, the adjustment should be small. In addition, the high-valuation passenger mainly affects high-price periods and the low-valuation passenger mainly affects low-price periods. When the proportion of strategic passengers is fixed, the lower the passenger strategic level is, the larger the price slope is. These findings can provide some references for the airline to make more precise and flexible pricing decisions.

정기선 해운기업 선박투자 행태분석에 관한 실증연구

This study analyzed the ship investment behavior of Korean liners before the bankruptcy of Hanjin Shipping in 2017 by dividing them into investment in new ships and charter ships. In the case of newbuilding investment, the ship investment behavior of global liners was measured in the boom period of 2003-2008 (6 years), the recession period of 2009-2014 (6 years), the recent 2015-2016 (2 years), the low newbuilding priced period, and the high newbuilding priced period. 2006-2007 (2 years) were analyzed. In the case of charter investment, the charter investment behavior of global liner companies for 10 years from 2007 to 2016 was analyzed and compared with major global shipping companies. As a result of the analysis, it was evaluated that Hanjin Shipping and Hyundai Merchant Marine (currently HMM) showed lower performance compared to global liner companies in terms of investment in new shipbuilding and charter ships, as they placed orders during the period of high ship prices and high charter rates, and the proportion of long-term chartered ships was high. This investment behavior affected Hanjin Shipping’s bankruptcy, and it is estimated that it acted as a financial burden on Hyundai Merchant Marine. It is expected that this study will provide useful information in terms of timing and strategy for ship investment by shipping companies in the future, and it is expected that it will have a great potential for use in establishing government policies.

Feasibility assessment of utilizing electric vehicles for energy arbitrage in smart grids considering battery degradation cost

ABSTRACT A smart grid environment can enable the participation of electric vehicle (EV) owners in energy arbitrage, provided that the utility employs a dynamic tariff structure and the EV chargers support the vehicle-to-grid (V2G) mode of operation. The EV owners can trade electricity with the utility profitably by exporting the energy stored in their vehicle batteries during the high-priced periods and compensating for the sold energy during the cheaper intervals. However, the additional charge-discharge cycles involved in this venture lead to faster degradation of EV batteries. This paper analyzes the economics behind the arbitrage business in terms of the returns from energy transactions and the cost of battery deterioration, and compares it with a scenario in which the EV users stay away from the V2G market. In addition to the financial analysis, it is also important to monitor the impact on the distribution network in terms of operational parameters such as peak demand, energy loss and voltage fluctuations. The study presented in this paper assesses the feasibility of engaging EVs in electricity arbitrage from the perspective of the owners’ economic welfare as well as the grid’s technical specifications. Results indicate that although the battery degradation cost increases due to energy trading, the revenue generated is substantial enough to give a net saving of 5% for the vehicle owners. With respect to grid impact, all the monitored parameters remain within the prescribed limits, and the peak demand reduces by nearly 15%.

Investigation of the effect of the envelope on building thermal storage performance under model predictive control by dynamic pricing

Dynamic pricing is designed for the load shaping to help match the amount of the energy demand to the energy supply capacity. Since the buildings’ characteristics influence the performance of the energy shifting, renovation of the building towards a higher energy flexibility is worth investigating. This study evaluated the effect of the envelope on building thermal storage performance. A model predictive control (MPC) was developed to achieve a multi-objective control i.e., indoor comfort temperature and minimise the total energy cost. MPC automatically triggered the energy storage during the low price periods and used the stored energy during the high price periods. The results confirmed the ability of MPC on peak demand reduction up to 45% electricity cost. Besides, the results also demonstrated the ability of heavyweight thermal mass in terms of reducing energy consumption and shifting a greater high price energy to the low price times. Therefore, adding insulation layers into the lightweight thermal mass is highly recommended, especially for the places experiencing the significant mismatch between the demand and supply during daily peaks or the areas scheduling a large amount of intermittent renewable energy source in the energy production.

Agent-Based System for Microgrid Power Real-Time Pricing with Client Preferences

Microgrid technology is actually the most practical way for Distributed Energy Resources (DERs) deployment in urban and rural communities. The factors that motivate reliance to the microgrid technology include energy security, economy and environment. The economical sustainability of microgrids depends on the energy market within a community. This can be challenged by issues related to the scalability of the microgrid and the setting up of a pricing system. Various energy management systems and energy pricing methods developed so far offer little or no option to the end user/client to accept or reject the proposed price. Thus, this paper presents a client focused agent that automatically negotiates various available economical options in real-time pricing context within a microgrid. The proposed agent collects and analyses the prices as communicated by the microgrid network operator. Moreover, based on client predefined preferences, it regulates the client load demand in a predefined sequence with the aim of minimizing the power consumption during high price period. Therefore, the proposed system will reduce the client overall energy cost and contributes towards demand side management from the network operator perspective. The agent mathematical model and algorithm are simulated in MATLAB environment. The findings of the study demonstrate the effectiveness of the proposed agent approach in offering the client a real-time control over the energy cost control in a microgrid context.

Does oil price uncertainty affect earnings management? Evidence from GCC markets

PurposeSince it is believed that economic growth in oil-rich countries is highly influenced by oil price movements, this study aims to explore the relationship between oil price volatility (uncertainty) and earnings-management decisions.Design/methodology/approachFinancial data from oil-exporting countries were used to explore the relationship between oil price volatility and earnings-management decisions. The study used univariate and multivariate analysis. The modified Jones model is the proxy accrual earnings management. Further, the standard deviation of daily oil price returns is used to proxy annualised oil price volatility.FindingsThe results show that there is an association between oil price volatility and accrual earnings management. Specifically, there is a positive and significant relationship between negative accruals and oil price volatility, indicating that firms are inclined to conduct income-decreasing earnings management in periods of high oil price volatility.Research limitations/implicationsThis study’s findings have important implications for regulators and investors because they indicate that the uncertainty of oil price volatility has an influence on earnings quality in oil-dependent economies. This is especially important considering the ongoing debate on transparency issues.Originality/valueTo the best of the authors’ knowledge, this study is the first to investigate the relationship between oil prices volatility and earning management behaviour for non-financial firms. Further, the study uses unique data of oil-dependent economies.

The value of in-reservoir energy storage for flexible dispatch of geothermal power

Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. We find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home-to-grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low-price period, the ESS and EV are charged and discharged during the high-price period. The paper built a multi-objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi-objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

Oil, politics, and “Corrupt Bastards”

Does oil corrupt? We test this theory using forty years of U.S. state-level data measuring corruption as both convictions of corruption and the frequency that words like “corrupt”, “fraud”, and “bribe”—and their iterations—appear in newspapers. We find that oil-rich U.S. states experience more corruption than their oil-poor counterparts, but only during periods of high oil prices, suggesting a causal relationship. Results are robust to a variety of modeling assumptions and specifications. Implications and mechanisms are discussed.

Flexible Power & Biomass-to-Methanol plants: Design optimization and economic viability of the electrolysis integration

This paper assesses the optimal design criteria of a flexible power and biomass to methanol (PBtM) plant, conceived to operate both without green hydrogen addition (baseline mode) and with hydrogen addition (enhanced mode), following an intermittent use of the electrolysis system, which is turned on when the electricity price allows an economically viable hydrogen production. The assessed plant includes a gasification section, syngas cleaning and compression, methanol synthesis and purification and heat recovery steam cycle, to be flexibly operated. A sorption-enhanced gasification technology allows to produce a tailored syngas for the downstream synthesis in both the baseline and enhanced operating conditions, by controlling the in-situ CO2 separation rate. Two designs are assessed for the methanol synthesis unit, with two different reactor sizes: (i)a larger reactor, designed on the enhanced operation mode (enhanced reactor design – ERD) and (ii)a smaller reactor, designed on the baseline operation mode (baseline reactor design – BRD). The ERD design resulted to be preferable from the techno economic perspectives, resulting in 20% lower cost of the e-MeOH (30.80 vs. 37.76€/GJLHV) with the baseline assumptions (i.e. 80% of electrolyzer capacity factor and 2019 Denmark day-ahead market electricity price). Other important outcomes are: (i)high electrolysis capacity factor is needed to obtain competitive cost of e-MeOH and (ii)advantages of flexibly operated PBtM plants with respect to inflexible PBtM plants are significant in scenarios with high penetration of intermittent renewables, leading to low average prices of electricity but also longer periods of high peak prices.

Single-machine scheduling with energy generation and storage systems

This paper considers a single-machine scheduling problem with sequence-dependent setup times and energy-generation and storage systems. Each job requires a sequence-dependent setup to be processed on the machine, and both setup and processing of the job require job-dependent amounts of energy. The energy consumed by the machine can be bought from an Electric Power Company (EPC) or generated by own Distributed Energy Resource (DER), such as solar photovoltaic or wind, and the energy can be stored in an Energy Storage System (ESS). The objective is to minimise the total cost, the sum of production cost depending on makespan and energy cost by considering energy usage from the EPC, DER and ESS. For the problem, a mathematical programming model is first derived by using period-based indexes. Then, a hybrid genetic algorithm, which adds appropriate idle times between jobs and determines an efficient energy schedule by storing some energy during less expensive periods into the ESS for later use in high-price periods, is developed. Finally, computational experiments show that the proposed algorithm provides effective solutions, and which component of the total cost affects the performance the most, how effective adding idle times between jobs is, and how much cost can be saved by having a DER and ESS.

Economic‐environmental stochastic scheduling for hydrogen storage‐based smart energy hub coordinated with integrated demand response program

According to the necessity of establishing an effective, dependable, and cost-effective system to supply various demands, the significance of an energy hub has emerged in recent years. Also, the other aspect dealing with decreasing the global warming issues should be taken into account. To this end, a smart multicarrier energy hub (SMEH) coordinated with an integrated demand response program (IDRP) and hydrogen storage system (HSS) as flexible resources has been proposed. Managing the consumption of electrical and thermal demands is handled by IDRP, whilst fuel cell–based HSS technology has an important role in lessening the air pollutant emissions. In reality, HSS could convert electrical power to hydrogen energy via power to hydrogen (P2H) at low price hours or in high renewable energy resources (RERs) penetrations; however, at high price periods or in low RERs penetrations, electricity is generated through hydrogen-based fuel cells. In addition to the aforementioned parts, combined heat and power (CHP), boiler, gas-fired units (GFUs), HSS, electrical heat pump (EHP), energy and thermal storage systems (ESS/TSS), and RERs are also considered in the SMEH. The scenario-based stochastic method is utilized to handle the fluctuations of electricity price and RERs' output. The multiobjective scheduling framework of the problem is modeled as a mixed-integer linear programming (MILP) to investigate and obtain the economic–environmental benefits. Consequently, the applicability of the introduced methodology is verified via sample case studies. Simulation outcomes represent that applying IDRP and HSS results in a decrement in overall cost and total emission by about 1.54% and 2.9%, respectively.

Electric vehicle routing with charging/discharging under time-variant electricity prices

The integration of electric vehicles (EVs) with the energy grid has become an important area of research due to the increasing EV penetration in today’s transportation systems. Under appropriate management of EV charging and discharging, the grid can currently satisfy the energy requirements of a considerable number of EVs. Furthermore, EVs can help enhance the reliability and stability of the energy grid through ancillary services such as energy storage. This paper proposes the EV routing problem with time windows under time-variant electricity prices (EVRPTW-TP) which optimizes the routing of an EV fleet that are delivering products to customers, jointly with the scheduling of the charging and discharging of the EVs from/to the grid. The proposed model is a multiperiod vehicle routing problem where EVs can stop at charging stations to either recharge their batteries or inject stored energy to the grid. Given the energy costs that vary based on time-of-use, the charging and discharging schedules of the EVs are optimized to benefit from the capability of storing energy by shifting energy demands from peak hours to off-peak hours when the energy price is lower. The vehicles can recover the energy costs and potentially realize profits by injecting energy back to the grid at high price periods. EVRPTW-TP is formulated as an optimization problem. A Lagrangian relaxation approach and a hybrid variable neighborhood search/tabu search heuristic are proposed to obtain high quality lower bounds and feasible solutions, respectively. Numerical experiments on instances from the literature are provided. The proposed heuristic is also evaluated on a case study of an EV fleet providing grocery delivery at the region of Kitchener-Waterloo in Ontario, Canada. Insights on the impacts of energy pricing, service time slots, range reduction in winter as well as fleet size are presented.

Specific financial and stock indicators as a sign of investment attractiveness of domestic vertically integrated companies

Subject. The article considers the specific financial and stock market indicators of the 25 biggest oil and gas companies from 2006 to 2018. Objectives. The aim is to identify key trends in the specific financial and stock market indicators of leading publicly traded oil and gas companies and reveal the factors that have led to this transformation, during the study period. Methods. The study draws on methods of comparative, financial, and economic analysis, as well as generalization of materials of consolidated financial statements. Results. The study established that the prolonged industry crisis had the strongest impact on the group’s performance, and independent companies were the most affected. Negative trends were evident in the period of high oil prices, when specific capital expenditures in the exploration and production sector increased, while profitability and market appreciation of the barrel of production declined. The industry crisis only aggravated the situation in the industry. It was revealed that the processing of purchased raw materials, as well as the resale of products, may have a significant impact on the specific net revenue from core activities. The study identified that having a serious difference in the level of debt, it is better to use an indicator of company's market value, rather than capitalization. Conclusions. There is a decrease in the specific profitability of hydrocarbon production in the stock market segment of the industry. It has an adverse effect on the market attractiveness of the oil and gas sector.

Моделювання використання системи акумулювання для перенесення надлишкової електричної енергії сонячної електростанції

The peculiarity of the operation of solar photovoltaic power plants is the dependence of the generation power on weather conditions, which leads to the maximum production of electrical energy at noon hours of the day. Due to a decrease in electricity consumption, insufficient unloading capacity of pumped storage power plants in the integrated energy system of Ukraine and the specifics of electricity production at solar photovoltaic power plants, dispatching restrictions on the level of generation power are already taking place. To transfer volumes of electrical energy in the world, electrical energy storage systems are used, which operate based on lithium-ion storage batteries. Such systems have a number of advantages over other battery energy systems, which allows their implementation in almost any power generation facility. With the help of energy storage systems, it is possible to make a profit through the purchase of electric energy during a period of low prices and its release during a period of high prices, allowing consumers to save money on its payment. In this paper, we simulate the use of a battery energy storage system for storing electrical energy to transfer excess electrical energy from a solar photovoltaic power plant. To conduct a study and identify excess capacity of a solar photovoltaic power plant, the daily schedule of electrical load is equalized to the capacity of a separate power plant Because of the study, the optimal time for charging and discharging the battery was determined, from which it can be seen that the need to transfer excess electricity to a solar photovoltaic power plant occurs at lunchtime, and their discharge at the peak is the graph of the electrical load of the power system. The aggregate operation of a solar power plant with a total installed capacity of photovoltaic power at the level of 10 MW (DC) and a battery energy storage system for accumulating electric energy with a capacity of 3.75 MWh was simulated. For the study day, the required capacity of a battery system for accumulating electric energy at the level of 1.58 MW was determined. Using the methodology of the levelized cost of electricity and storage, a technical and economic assessment of the transfer of excess capacity of a solar photovoltaic power plant using a battery system for storing electrical energy was carried out. When calculating the cost of storage, the cost of the transferred electrical energy from the solar power plant was taken into account. From the results of technical and economic calculations, it can be seen that, in terms of the cost of equipment, as of 2020, the cost of supplying excess electrical energy from the battery energy storage system is growing when compared with the supply from a solar photovoltaic power plant. Taking into account some forecast assumptions, the cost of electricity supply from the battery energy storage system was calculated for the mode of transferring excess capacity of a solar photovoltaic power plant for 2025 and 2030 years. Keywords: modeling, power system, load demand curve, solar photovoltaic power plant, electric energy storage system, cost

Day-Ahead Scheduling of a Gravity Energy Storage System Considering the Uncertainty

To overcome the topographic limitations of pumped hydro storage (PHS) system, novel gravity energy storage (GES) technologies are developing. In this paper, a pioneering work on the modeling and scheduling of a GES system is conducted. The GES system stores and releases energy by lifting and lowering concrete bricks. Since the working principle of the GES system is similar to that of a PHS plant, a double-tower based model of the GES system is established. In addition, a day-ahead scheduling model of the GES system is proposed to test the operation of such system. Specifically, the uncertainty of market price is considered with a data-driven distributionally robust optimization method. Some linearization and reformulation techniques are presented to transform the problem into a mixed integer linear programming (MILP) problem which can be solved easily. Numerical experiments indicate that the proposed model can effectively characterize the operation of the GES system. Concrete bricks are lifted up during low-price period and descend under gravity during high-price period to gain benefits. Additionally, the round trip efficiency considerably influences the total profits.

What are the triggers for arrears on debt over a business cycle? Evidence from panel data

AbstractThe paper investigates the triggers of arrears on debt in a full recourse euro area country. An extensive individual‐level quarterly panel dataset enables quarterly arrears on debt to be tracked while controlling for individual specific heterogeneity. The estimations show that lower income and higher debt service ratios are associated with a higher probability of arrears, confirming the “ability to pay” hypothesis. The results reveal different debt repayment behaviour for housing and consumer loans over a business cycle. Newly taken housing loans exhibit a lower probability of arrears and the same applies to loans granted during the period of easy credit conditions and high real estate prices, indicating that housing credit boom has not contributed to the rise in arrears on debt. Newly taken consumer loans are related to a higher probability of arrears, suggesting that consumer loans have been used to alleviate financial difficulties.

Analiza ekonomske opravdanosti investiranja u solarnu elektranu sa vertikalno postavljenim bifacijalnim fotonaponskim modulima u perspektivnim uslovima slobodnog tržišta

After the expiration of governmental incentive measures for renewable energy sources integration, economic feasibility of investing into solar power plants will highly depend on compatibility between production and variable prices. In order to achieve the maximum possible profit of the power plant in liberalized electricity market, it is necessary to consider the possibility of investing in solutions that are not common today, but with the potential of being more profitable in the future. Such a solution is a solar power plant consisting of vertically placed bifacial modules whose active surfaces are oriented in the east-west direction. This configuration of the power plant can achieve higher production in periods of high prices, and thus higher profits from the sale of electricity. On the other hand, such a solution is more expensive than a standard solar power plant with monofacial modules. In this paper, a comparison of return on investment in a bifacial power plant and a monofacial power plant with existing and prospective market conditions is performed. The influence of solar power plant production on the price of electricity was investigated on the example of Germany. Based on this research, a prognostic model of the daily price diagram on the unified European market until 2040 was formed. It served for the analysis of the profitability of investments in the two considered variants of the solar power plant realization.