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The study aims to find out an appropriate volume of power to be procured through long-term power purchase agreements (PPAs), the volume to be sourced from the power exchange through day-ahead and term-ahead options and also a suitable volume to be sold at different points of time within a day, which would finally lead to the optimum cost of power procurement.

The study has considered a Delhi-based power distribution utility and has collected all relevant data from its archival sources. A stochastic optimization model has been developed to capture the problem of power procurement faced by the distribution utility, which is modelled as a mixed integer linear programming problem. Sensitivity analyses were carried out on the important parameters including hourly demand of power, unit variable cost of power available through PPAs, maximum back-down percentage allowed under PPAs, etc., to investigate their impact on daily cost of power under PPAs, daily cost of power under day-ahead and term-ahead options, daily sales revenue and also the net total daily cost of power procurement.

The findings include the appropriate volume of power procured from different suppliers through PPAs and from the power exchange under day-ahead and term-ahead options and also the surplus volume of power sold under the day-ahead arrangement. It has also computed the total cost of power purchased under PPAs, the cost of power purchased from the power exchange under day-ahead and term-ahead options and also the revenue generated out of the sale of surplus power under the day-ahead arrangement. In addition, it has also presented the results of sensitivity analyses, which provide rich managerial insights.

The paper makes two significant contributions to the existing body of power procurement literature. First, the stochastic mixed-integer linear programming model helps decision makers in determining the right volume of power to be purchased from different sources. Second, based on the findings of the procurement model, a power procurement framework is developed considering the dimensions of uncertainty in power supply and the cost of power procurement. This power procurement framework would aid managers in making procurement decisions under different scenarios.

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.

The non‐storable nature of electricity and the increasing complexity of financial instruments as a tool for hedging against risk make the area of research very useful in the real world. Many power portfolio optimization problems have been developed to combat the issue of risk tolerance, but very few (if any) have included transmission constraints. The purpose of this paper is to consider optimization of portfolios of real and contractual assets, including derivative instruments, in a multi‐period setting where transmission constraints also exist.

Rather than using a flowgate constraint as a representation of transmission congestion, the authors use fixed transmission rights. A model is introduced that involves a three‐node unidirectional network in order to evaluate the significance of transmission constraints. Data from the PJM, which is located in the eastern USA, were used for model implementation.

The stochastic nonlinear mixed‐integer model presented shows that transmission constraints and fixed transmission rights can have a significant effect on the choices a utility will make when dealing with power procurement. It is demonstrated that the inclusions drastically decrease the value of the objective function.

Conditional value at risk (CVaR) was chosen over VaR as a risk measurement for two different reasons. First, it is important to have a good representation of the trade‐off between the best expected profit and the volatility experienced when obtaining that profit. Second, it provides protection against very undesirable scenarios that may occur with low probability. In order to simplify the fixed transmission rights contracts, a three‐node network is used with unidirectional flow.

When markets were regulated, transmission lines were owned and operated by local utilities, and all power sent over the lines was either owned by the operating utility or wheeled for another utility based on existing agreements. With the advent of deregulation, utilities were forced to wheel other companies' power, which introduced more risk in terms of transmission constraints.

The contribution of this research is to help companies not only hedge the risk of unknown power prices but also unknown transmission congestion. One distinctive feature of the authors' research is to expand upon existing “power portfolio optimization with risk” literature by introducing a transmission constraint into the model. Historically, transmission congestion has been modeled in different ways, including flowgates, transmission rents and fixed transmission rights.

The electric power industry has been moving from a regulated monopoly structure to a deregulated market structure in many countries. The purpose of this study is to comprehensively review the existing markets to study advantages, issues involved and lessons learnt to benefit emerging electricity markets.

The paper employs a comprehensive review of existing competitive electricity market models in USA (California), UK, Australia, Nordic Countries (Norway), and developing country (Chile) to analyze the similarities, differences, weaknesses, and strengths among these markets based on publically available data, literature review and information.

Ongoing or forthcoming electricity sector restructuring activities in some countries can be better designed based on lessons learnt from existing markets and incorporating their own political, technical and economical contexts. A template for design of successful electricity market has also been presented.

This study is limited to a comparative analysis of five markets and can be extended in the future for other existing and emerging electricity markets.

The discussed weaknesses and strengths of existing electricity markets in this study can be practically utilized to improve the electricity industry market structures leading to several social benefits including lower electricity cost.

The comprehensive review and analysis of five existing markets, physically located in different continents, may be used as an assistance or reference guide to benefit the emerging electricity markets in other countries.

The physical nature of electricity generation and delivery creates special problems for the design of efficient markets, notably the need to manage delivery in real time and the volatile congestion and associated costs that result. Proposals for the operation of the deregulated electricity industry tend towards one of two paradigms: centralized and decentralized. Transmission congestion management can be implemented in the more centralized point‐to‐point approach, as in New York state, where derivative transmission congestion contracts (TCCs) are traded, or in the more decentralized flowgate‐based approach. While it is widely accepted that theoretically TCCs have attractive properties as hedging instruments against congestion cost uncertainty, whether efficient markets for them can be established in practice has been questioned. Based on an empirical analysis of publicly available data from years 2000 and 2001, it appears that New York TCCs provided market participants with a potentially effective hedge against volatile congestion rents. However, the prices paid for TCCs systematically diverged from the resulting congestion rents for distant locations and at high prices. The price paid for the hedge not being in line with the congestion rents, i.e., unreasonably high risk premiums are being paid, suggests an inefficient market. The low liquidity of TCC markets and the deviation of TCC feasibility requirements from actual energy flows are possible explanations.

This chapter examines the efficiency of the Midcontinent Independent System Operator (MISO), Inc., electricity exchange following its major expansion in terms of market participants and geographic scope in 2014. Specifically, hourly day-ahead (forward) and real-time (spot) prices from 2014 to 2016 reveal that forward premiums are prevalent despite the increase in market size. Furthermore, these forward premiums do not adhere to Bessembinder and Lemmon’s (2002) commonly used general equilibrium model for electricity forward premia. A technical trading rule based on the relationship between day-ahead prices across hubs that was found to be profitable prior to MISO’s expansion still produces economically and statistically significant returns after the exchange’s growth.

This article aims to verify if there are detectable barriers in price levels that are understood to be psychologically important (psychological barriers) in a set of hourly electricity prices. These barriers manifest themselves when the market struggles with a difficulty in crossing the barrier to a different level. Psychological barriers focus on directional price movements around regions of the barrier, thus the importance of understanding investor behavior. The authors intend to contribute empirically to the scarce literature on psychological influences in individuals trading in the energy market, hereby enhancing the knowledge concerning the behavior of investors in this market.

The present work aims to test psychological barriers in the Nord Pool electricity market. Through a sample of hourly data on the Elspot day-ahead market, from 2013 to 2017, three groups of tests were made, following the M-values methodology: (1) uniformity tests, which clearly rejected the uniformity in hourly prices; (2) barrier tests, which included the barrier proximity and barrier hump tests, evidencing psychological barriers and (3) conditional effects tests, which allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average.

Uniformity tests, rejected the uniformity in hourly prices; barrier tests, included the barrier proximity and barrier hump tests, evidencing psychological barriers and conditional effects tests, allowed us to conclude in favor of effects of positive returns after approaching a barrier on an upward movement, i.e. the barrier breaches due to the fact that increasing prices tend to lead to further price increases, on average. Another relevant conclusion is that the period from midnight to 9 a.m. is very sensitive, since there is evidence of return and variance effects simultaneously. The implications of these results are potentially relevant, since changes on the variance are usually perceived as a proxy for risk, with changes on the return. It was also concluded that with the increase of the time span from 5 to 10 days on the conditional effects difference tests, there were significant changes on the results, the variance effect is stronger, while the return effect weakens.

However, this research presents some limitations that result in representing opportunities for future research. The fact that there are reduced data available for other markets end up limiting the study of the global electricity market. Although Nord Pool is Europe's leading energy market and is seen as one of the most successful energy markets in the world, it would be interesting to do a study with more than one electricity market to make comparative considerations. Although the spot market is the main arena for energy trade, while the intraday market works as a compliment, it would be equally interesting to do a similar study for the intraday market and then compare conclusions. Moreover, in the present study, it was used standard methods in the literature on psychological barriers, but other methods could have been used–for example, those that assume that prices follow the Benford's distribution (Lu and Giles, 2010), which also present a path for future research and opportunity for confirming the robustness of the present results.

When the presence of psychological barriers is detected it means that the risk-return relationship becomes weaker around the psychological barrier (round numbers, meaning that electricity traders anchor). Identification of psychological barriers supports the claim that technical analysis strategies based on price support and resistance can be profitable. Therefore, more profitable strategies can be built by traders, but no reconciliation with the efficient market hypothesis (EMH) (provided that in inefficient markets prices should not exhibit any particular pattern). The finding of significant psychological barriers in specific hourly time intervals implies the need to address its practical implications in electricity markets, being so specific, namely, the possibility to earn extraordinarily profits exploiting this anomaly and who wins.

The electricity sector is a determinant sector in economic growth and a factor of development. Herein lies the importance of studying this market, which until now has not occurred in this subject, as far as it was possible to gauge. Are there barriers in the electricity market and should such a presence be taken into account? Investigating the existence of psychological barriers in the electric market becomes relevant, because knowing that investors are psychologically affected by a psychological barrier, can become a useful tool in negotiation, as it can function as another variable in the “equation” which is to trade in a complex market like this. Proving the potential presence of a psychological barrier may lead investors to believe in the idea of levels of resistance or levels of support, affecting their decision-making and price dynamics.

Microgrids are inclined to use renewable energy resources within the availability limits. In conventional studies, energy interchange among microgrids was not considered because of one-directional power flows. Hence, this paper aims to study the optimal day-ahead energy scheduling of a centralized networked multi-carrier microgrid (NMCMG). The energy scheduling faces new challenges by inclusion of responsive loads, integration of renewable sources (wind and solar) and interaction of multi-carrier microgrids (MCMGs).

The optimization model is formulated as a mixed integer nonlinear programing and is solved using GAMS software. Numerical simulations are performed on a system with three MCMGs, including combined heat and power, photovoltaic arrays, wind turbines and energy storages to fulfill the required electrical and thermal load demands. In the proposed system, the MCMGs are in grid-connected mode to exchange power when required.

The proposed model is capable of minimizing the system costs by using a novel demand side management model and integrating the multiple-energy infrastructure, as well as handling the energy management of the network. Furthermore, the novel demand side management model gives more accurate optimal results. The operational performance and total cost of the NMCMG in simultaneous operation of multiple carriers has been effectively improved.

Introduction and modeling of the multiple energy demands within the MCMG. A novel time- and incentive-based demand side management, characterized by shifting techniques, is applied to reshape the load curve, as well as for preventing the excessive use of energy in peak hours. This paper analyzes the need to study how inclusion of multiple energy infrastructure integration and responsive load can impact the future distribution network costs.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.