Generation and pump scheduling in a shipyard with interruptible loads

To ensure the safe and stable operation of the power system in the power market environment, a transaction model of power reserve ancillary services oriented to interruptible loads is constructed. By analyzing the benefits of interruptible users and power grid enterprises, a transaction model of interruptible load is established based on it. After obtaining the value of the minimum cost of power system to purchase interruptible load, the capacity cost, opportunity cost and standby use cost of power reserve are calculated respectively. The objective function of auxiliary power reserve service transaction model with interruptible load is established and the constraint conditions are set, and then the transaction result of auxiliary power reserve service is obtained by solving the objective function of the model. The experimental results show that the model can effectively calculate the power interruption compensation price of user interruptible load, and obtain the interruptible load transaction results according to the rapid capacity quotation, power quotation, etc. At the same time, the model has the lowest cost of reserve capacity and reserve use cost in a certain period of time, and the application effect is good.

There are two compensation methods for interruptible loads (ILs), namely low price compensation before supply unavailability and high price compensation after supply unavailability. Low price compensation is independent of power supply unavailability, while the high compensation is performed only after actual power supply unavailability. However, the IL with low price (ILL) and the IL with high price compensation (ILH) have only been studied separately till now. Based on risk management, this paper analyzes the different economic properties of the two compensation methods and concludes that their coordination is beneficial to restrain market power and reduce the cost of reserve capacity. The authors propose the coordination models and optimization algorithms by taking the sum of the deterministic reduction of revenue resulting from ILL and the risk of compensating ILH as the objective function. Simulation results are presented to validate the effectiveness of the proposed method.

The impact of virtual bidding on price volatility in New York's wholesale electricity market

With the increasing penetration of renewable energy sources such as wind and solar power, higher requirements of flexible supply ability for power systems are put forward. In this context, an optimal dispatching method considering two types of demand response is proposed to improve power system flexibility. On the demand side, both the time-of-use (TOU) price and interruptible load are used to guide consumers to actively optimize the load curve, so as to relieve the pressure of the flexible supply of conventional units on the supply side. The demand response model consisting of TOU price and interruptible load is built. Then, taking the minimum cost as the objective function, the optimization model for power system dispatching is solved. Finally, the modified IEEE 30 bus system is taken as a case study. The results show that the consideration of two-type demand response in the optimal dispatching can improve the flexibility while ensuring economy, which verifies the effectiveness of the proposed method.

Interruptible load (IL) management as an important component of demand-side management in electricity market affects much the safe and economic operation of the power systems. The cost-benefit of interruptible load participants is analyzed in this paper. The discrete customer category is proposed to indicate the expected compensation for the blackout. This paper presents a model of IL contract, in which the objective function is the minimum compensation cost paid by the power company. The benefits of the power company and the customer are both considered as the restrictions of the model. The actual system examples show the validity of the proposed model.

By performing Grey relation analysis, this study elucidates the relationship between investor sentiment and price volatility in the Taiwanese stock market. A sequential relationship is identified between investor sentiment and price volatility, and ranked according to order of importance. Analytical results show that short sales volumes may be an individual leading indicator useful in observing the effects of sentiment on price volatility, followed by open interest put/call ratios and trading volumes, and buy/sell orders. Institutional investors are related, to a lesser extent, to price volatility and sentiment. Qualified foreign institutional investors, or more rational investors, are the least influenced by price volatility, followed by securities investment trust companies and dealers. TAIEX options exert the strongest influence on sentiment during the study period, making them a valuable reference for gauging price volatility.

An understanding of changes in price volatility is of value to policy makers and exchange committee members as well as other participants in commodity futures markets. Previous research has studied volatility by measuring: 1) the flow of new information into the market, or 2) the effect that the structure of the futures market has on price volatility. In this paper, a model is developed which integrates these two themes in the literature to measure and explain price volatility in live cattle futures prices. The model is subjected to a battery of diagnostic tests so that a comparison can be made between the integrated model and models from previous research. Also, since price volatility from the underlying commodity is a major component in the determination of option premiums, a comparison is made between the integrated model and a naive model to forecast live cattle option premiums.

This paper presents an agent-based model of the financial order book, and investigates whether the volatility of market prices can be reduced by means of specific policy interventions. The model is able to replicate the salient stylized facts of empirical data. Simulations show how market volatility is affected by two types of factors: individual characteristics of traders (i.e., behavioral heterogeneity, willingness to pay, and reaction to informative signals), and micro-structural features of the order book (i.e., the duration of limit orders and the number of allowed counterparts for market orders).

Uncertainty indexes are used in economics to assess the uncertainty regarding economic policy, and in finance to quantify the uncertainty associated with the price volatility of financial instruments and commodities. Existing uncertainty indexes are typically constructed using realized (historical) price volatility, implied volatility when options are available, and conditional volatility using various econometric models. In this article, the authors present an improved commodity price uncertainty index, an oil price uncertainty index that includes information from news articles and social media. They demonstrate the superior performance of their new index compared with existing indexes that do not include news and social media information. They also create an oil price volatility forecasting model based on media coverage and use it to trade an oil volatility portfolio, obtaining a return that exceeds the average monthly growth of oil price volatility. Altogether, media coverage is a powerful predictor of future price volatility in the oil market. TOPICS:Volatility measures, performance measurement Key Findings • Media information from newspapers and Twitter about oil price uncertainty is combined with the existing OVX oil ETF volatility index to create a new oil price uncertainty index (OPUX). • The OPUX model gives a significantly better prediction of 1-month forward oil price volatility, as measured by an increase in R-squared from 12.1% for OVX to 33.0% for OPUX. • A volatility trading model based on OPUX exceeds the average oil price volatility by 0.29% per month.

Dry bulk shipping market is a key element of international trade. Newbuilding vessel prices tend to fluctuate drastically, however the price volatility mechanism and major determinants were neglected, regardless of the abundant research on newbuilding price formation and price determinants. This paper applied the GARCH (1, 1) model to investigate the key determinants of newbuilding vessel price volatility and how freight rate affects it. The empirical results revealed that freight rate volatility is the most important and the positive determinant for newbuilding price volatility in capesize, panamax, handymax and handysize sectors. Shipyard capacity change, exchange rate volatility and shipbuilding cost volatility are key determinants for some vessel types, while secondhand vessel price volatility is not the key determinant for any vessel type. Our findings could help the practitioners better understanding the market dynamics and managing the market risks.

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

ABSTRACTAquaculture production is increasingly important in seafood production and is currently the fastest growing food production technology worldwide. To continue the industry growth, price risk management is an essential part in creating successful projects and sustainable business operations. This research uses trade data on fish and invertebrate prices, and evaluates the relative price uncertainty from farmed and captured products. Moreover, we consider a set of species groups (fish, invertebrates), species’ families (e.g., Salmonidae, white fish and crustaceans), and individual species (e.g., salmon, seabream and prawns), and evaluate their price volatility over time using Value-at-Risk (VaR). The results indicate that the introduction of aquaculture production to a species’ total supply will reduce price volatility. Consequently, incentives and regulations should support research and development of new aquaculture species.

Demand side participation can significantly affects the energy and Spinning Reserve (SR) markets operation. Generally, the consumers can participate in energy and SR markets by offering their Interruptible Loads (ILs) and demand-side reserves respectively. In this paper a developed joint day-ahead energy and SR markets clearing model is presented. For modeling the proposed approach the Independent System Operator (ISO) simultaneously minimizes the energy and SR markets operation costs. Meanwhile the total Expected Load Not Supplied (ELNS) cost in accordance with involuntary load shedding of consumers in contingencies is considered in objective function based on the Value of Loss Load (VOLL) of different consumers. The energy market operation cost consists of offered generation and interruption costs which are declared by Generation Companies (GENCOs) and Distribution Companies (DISCOs) respectively. Moreover the SR market operation cost includes the procurement cost of up and down SR by GENCOs and offered demand-side SR cost by DISCOs. The proposed model is a Mixed Integer Nonlinear Programming (MINLP). An 8-bus test system is used to evaluate the efficiency and efficacy of the proposed model in energy and SR markets scheduling.

The load characteristic model based on demand response and the load optimization control method in three different scenarios are studied in this paper. Firstly, by decoupling rigid load and flexible load, according to different degree that flexible load participates in demand response, considering the influence of different control variables, three types of load characteristics models of electricity price load, direct control load and interruptible load are constructed. Secondly, based on the established the load characteristic model proposes a reasonable load control strategy optimization control method under the condition of different practical power supply gap constraints. Finally, through three cases of scenarios, we explored the control variables are direct control load and interruptible load control, the objective function is when the deviation between the actual load reduction amount and the power supply gap is the smallest, and the constraint condition considers the demand response contract. The feasibility of the constructed model and the optimization method is verified, which provides a reference for the promotion of user-side demand response in China.

Coordinated optimization of source–storage–load in distribution network based on edge computation