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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 26459

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Prof. Dr. Józef Paska

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Division of Electric Power Plants and Economics of Electrical Power Engineering, Institute of Electrical Power Engineering, Faculty of Electrical Engineering, Warsaw University of Technology, 00-662 Warszawa, Poland
Interests: uncertainty and risk analysis; risk assessment and management; power systems and electricity markets; forecasting; energy policy and regulatory;

Special Issue Information

Dear Colleagues,

We are pleased to call for papers for a Special Issue on “Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets”. This Special Issue is dedicated to all areas of power systems and electricity markets.

Topics of interest for this Special Issue include but are not limited to the following:

Uncertainty and risk analysis and modeling in power systems and electricity markets;
Risk assessment and management in power systems and electricity markets;
Modeling and assessment of stochasticity in power systems and electricity markets;
Forecasting in power systems and electricity markets;
Energy policy and regulatory impact on power systems and electricity markets;
Economic and financial aspects of power systems and electricity markets;
Emerging electricity markets, e.g., consumer-led.

Prof. Dr. Józef Paska
Guest Editor

Manuscript Submission Information

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Keywords

Uncertainty and risk analysis and modeling in power systems and electricity markets
Risk assessment and management in power systems and electricity markets
Modeling and assessment of stochasticity in power systems and electricity markets
Forecasting in power systems and electricity markets
Energy policy and regulatory impact on power systems and electricity markets
Economic and financial aspects of power systems and electricity markets
Emerging electricity markets

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Published Papers (10 papers)

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Research

25 pages, 5676 KiB

Open AccessArticle

Multiperiod Portfolio of Energy Purchasing Strategies including Climate Scenarios

by Juan M. Gómez and Yeny E. Rodríguez

Energies 2022, 15(9), 3012; https://doi.org/10.3390/en15093012 - 20 Apr 2022

Cited by 1 | Viewed by 1599

Abstract

Because electricity retailers must ensure that energy supply matches end-user demand, electricity that is not traded through bilateral contracts is typically traded in power exchanges which are subject to great price volatility. In Colombia, the spot price is a reflection of climate variability because approximately 70% of the country’s electricity is generated by large hydropower stations. In this study, we forecast 2018′s prices and calculated its corresponding purchase margins using the 2015 to 2017 bilateral contract prices for electricity plus power exchange price information and climate information. Our forecasts included climate uncertainty and evaluated two multi-period portfolio methods for deciding among three purchasing strategies: bilateral contracts in the regulated market, bilateral contracts in the non-regulated markets, and purchases in the power exchange. The results indicate that retailers should follow a middle course that is neither conservative nor risky. Creation of portfolios independent of the multi-period method can balance purchases through bilateral contracts and in the power exchange in a way that considers climatic uncertainty. This type of balanced portfolio could control medium-term risks of price volatility and result in good levels of purchase margins. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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17 pages, 1089 KiB

Open AccessArticle

Application of Real Options Approach to Analyse Economic Efficiency of Power Plant with CCS Installation under Uncertainty

by Janusz Sowinski

Energies 2022, 15(3), 1050; https://doi.org/10.3390/en15031050 - 30 Jan 2022

Cited by 6 | Viewed by 2591

Abstract

The main goal of this article is to build a decision model for an investment involving the addition of a CCS (Carbon dioxide Capture and Storage) installation in an existing conventional power plant. The application of CCS systems in coal and gas power plants involves large capital expenditures and an increase in operating costs. The lack of upgrade modernisation and environmentally friendly investments in this type of power plant generates the additional costs of the purchase of emission allowances. An analysis of the impact of the addition of a CCS installation to an existing coal power plant on the costs of electricity generation is presented. Based on the accessible technical and economic data, a concept has been framed and an original decision-making model has been developed for an investment consisting in constructing a CCS installation in an existing power plant. A novelty of the paper is the presented proprietary decision-making model in conditions of uncertainty using the real options approach. Stochastic state variables are included in the model: the price of the CO₂ emission allowance, the unit costs of capturing, transporting, storing and stockpiling CO₂ and the unit costs of electricity generation. It is assumed that the time curves of the state variables are described by equations of geometric Brownian motions. The values of standard deviations in the equations are measures of uncertainty. The value of the retrofit option is defined as the maximum value from the expected net present value. From the dynamic optimisation equation, resulting from Bellman’s principle of optimality, it results that the retrofit option must satisfy the differential equation. The calculations were made for a specific, commercially applicable case of CCS technology in order to present the model’s capabilities. The analyses’ results and conclusions are presented. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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22 pages, 613 KiB

Open AccessArticle

Multi-Objective Approach for Managing Uncertain Delivery from Renewable Energy Sources within a Peer-to-Peer Energy Balancing Architecture

by Mariusz Drabecki and Eugeniusz Toczyłowski

Energies 2022, 15(3), 675; https://doi.org/10.3390/en15030675 - 18 Jan 2022

Cited by 7 | Viewed by 2080

Abstract

On the energy markets, conscious customers may exist who are not only interested in minimising the cost of energy purchase, but, simultaneously, in optimising some other quality criteria (arising from ecological concerns, or social responsibility of the energy producers). In this paper, we develop both a mathematical optimisation problem and a market framework for balancing a power system in a peer-to-peer market setup, where product differentiation can be considered directly on the market. Thus, origins of energy may be clearly identified, and product quality characteristics can be understood by various actors (including households). We derive a multi-objective (mixed-integer) linear programming optimisation problem for balancing the energy system in a peer-to-peer energy trading environment, where not only the cost but also other additional quality criteria are considered. We have identified many possible actors to be present within the proposed market setup. They include consumers, producers, brokers and flexible prosumers with storage. The approach was tested on the IEEE 30-bus standard test system, over three different scenarios, by analysing the impact of various actors/peers activities and different extensions. It has been shown that a multi-objective energy balancing scheme may be developed through crafted optimisation problem and that each type of studied peers may bring some added value to the power system balancing. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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30 pages, 2659 KiB

Open AccessArticle

Framework for the Introduction of Vehicle-to-Grid Technology into the Polish Electricity Market

by Krzysztof Zagrajek, Józef Paska, Łukasz Sosnowski, Konrad Gobosz and Konrad Wróblewski

Energies 2021, 14(12), 3673; https://doi.org/10.3390/en14123673 - 20 Jun 2021

Cited by 19 | Viewed by 3863

Abstract

Vehicle-to-grid (V2G) technology is one of the advanced solutions that uses electric vehicles (EV) to balance electricity demand in the power system. It can be particularly useful in analyzing and then mitigating the risk of not delivering electricity to the end user. Therefore, it is necessary to analyze the possibility of operation of this technology in the legal framework. The article presents the analysis of the legal status in Poland, referring to the documents of the European Union and domestic legislation. Potential changes in Polish energy law that could facilitate the implementation of V2G technology are also proposed. In addition, the authors suggested the principles for the use of this technology, formulating a mechanism called the V2G Program. Within this Program, the V2G Service was defined and a business model of its implementation by a participant of the V2G Program (uEV) was presented. In addition, an uEV selection algorithm is provided so that the mathematical model of the V2G Service can be validated. Based on the performed simulations, it can be concluded that the implementation of the V2G Program requires significant changes in the Polish energy law, but it is feasible from the technical point of view. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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18 pages, 4363 KiB

Open AccessArticle

Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction

by Bogdan Bochenek, Jakub Jurasz, Adam Jaczewski, Gabriel Stachura, Piotr Sekuła, Tomasz Strzyżewski, Marcin Wdowikowski and Mariusz Figurski

Energies 2021, 14(8), 2164; https://doi.org/10.3390/en14082164 - 13 Apr 2021

Cited by 34 | Viewed by 4868

Abstract

The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management–National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018–2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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29 pages, 3025 KiB

Open AccessArticle

Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

by Mirosław Parol, Paweł Piotrowski, Piotr Kapler and Mariusz Piotrowski

Energies 2021, 14(5), 1290; https://doi.org/10.3390/en14051290 - 26 Feb 2021

Cited by 12 | Viewed by 1908

Abstract

This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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19 pages, 1063 KiB

Open AccessArticle

Fixed Transmission Charges Based on the Degree of Network Utilization

by Roman Korab, Henryk Kocot and Henryk Majchrzak

Energies 2021, 14(3), 614; https://doi.org/10.3390/en14030614 - 26 Jan 2021

Cited by 3 | Viewed by 2141

Abstract

The core objective of transmission tariffs is the recovery of costs related to the transport of electricity. A usual component of a tariff is a fixed charge that covers the costs of the network infrastructure. As many customers use the power grid, the rate of this charge should reflect, as closely as possible, the actual costs of supplying energy to the individual consumers. These costs result from which network elements have been used in delivering the electricity, and to what extent these elements have been used. Therefore, the fixed transmission rates should depend on the degree of network utilization. This article investigates definitions of the degree of network utilization based on the active power flow. To calculate the degree of network utilization, the flow of electricity on a branch must be decomposed into the streams flowing to individual customers. For this decomposition, two methods are examined: a power flow tracing method, based on the proportional sharing principle, and an incremental power flow method, based on the superposition principle. The analyzed methodology is applied to a small test system for conceptual discussions, as well as to the transmission network of the Polish power system, as an example of practical application. The results of this study were then compared with the commonly used “postage stamp” method. Finally, several practical aspects related to the potential implementation of the presented methodology are discussed. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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21 pages, 1618 KiB

Open AccessArticle

The Polish Practice of Probabilistic Approach in Power System Development Planning

by Maksymilian Przygrodzki and Paweł Kubek

Energies 2021, 14(1), 161; https://doi.org/10.3390/en14010161 - 30 Dec 2020

Cited by 5 | Viewed by 1847

Abstract

Power systems can be analyzed using either a deterministic or a probabilistic approach. The deterministic analysis centers on studying the quantities and indicators that characterize the operating states of the power system under strictly defined conditions. However, the long-term horizon of planning analyses, the changes of marketing mechanisms, the development of renewable electricity sources, the leaving from large-scale generation, the growth of smart technology and the increase in consumer awareness make the development of transmission networks a non-deterministic problem. In this article, we propose a planning procedure that takes the probabilistic elements into account. This procedure was developed to take into account the high variability of power flows caused by the generation of renewable sources and international exchange. Such conditions of the power system operation forced a departure from deterministic planning. The new probabilistic approach uses the existing tools and experience gained during subsequent development projects. As part of the probabilistic approach, simulations were carried out using the Latin Hypercube Sampling and Two Point Estimation Method algorithms. These methods effectively reduce the computation time and, at the same time, give satisfactory results. The verification was carried out on a test grid model developed in accordance with the technical standards used in the Polish Power System. Effects were assessed using a deterministic and probabilistic approach. This analysis confirmed the practical possibility of using the probabilistic approach in planning the development of transmission network in Poland. When using a probabilistic approach to predict power flow, the criteria of technical acceptability for a given development variant and the manner in which the strategy is determined are of particular importance. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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20 pages, 543 KiB

Open AccessArticle

Marginal Uncertainty Cost Functions for Solar Photovoltaic, Wind Energy, Hydro Generators, and Plug-In Electric Vehicles

by Elkin D. Reyes, Arturo S. Bretas and Sergio Rivera

Energies 2020, 13(23), 6375; https://doi.org/10.3390/en13236375 - 2 Dec 2020

Cited by 11 | Viewed by 1958

Abstract

The high penetration of renewable sources of energy in electrical power systems implies an increase in the uncertainty variables of the economic dispatch (ED). Uncertainty costs are a metric to quantify the variability introduced from renewable energy generation, that is to say: wind energy generation (WEG), run-of-the-river hydro generators (RHG), and solar photovoltaic generation (PVG). On other side, there are associated uncertainties to the charge/uncharge of plug-in electric vehicles (PEV). Thus, in this paper, the uncertainty cost functions (UCF) and their marginal expressions as a way of modeling and assessment of stochasticity in power systems with high penetration of smart grids elements is presented. In this work, a mathematical analysis is presented using the first and second derivatives of the UCF, where the marginal uncertainty cost functions (MUCF) and the UCF’s minimums for PVG, WEG, PEV, and RHG are derived. Further, a model validation is presented, considering comparative test results from the state of the art of the UCF minimum, developed in a previous study, to the minimum reached with the presented (MUCF) solution. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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22 pages, 1657 KiB

Open AccessArticle

Hybrid Multi-Criteria Method of Analyzing the Location of Distributed Renewable Energy Sources

by Alicja Stoltmann

Energies 2020, 13(16), 4109; https://doi.org/10.3390/en13164109 - 8 Aug 2020

Cited by 6 | Viewed by 2148

Abstract

This paper presents the development and the application of a hybrid multi-criteria method, the combination of the Analytic Hierarchy Process (AHP), and numerical taxonomy (NT), to support the decision making on the location of distributed renewable energy sources meeting various types of assessment criteria. Finding criteria weights, using the AHP method, eliminates the disadvantage of NT—which, in current form, is defined by its extreme values. The NT method is less mathematically complicated than the AHP method, and thus, less time-consuming. The combination of methods was used to investigate: (1) Which location among these analyzed has the best chance of implementation considering the author’s set of criteria to describe the proposed locations in detail; and (2) which detailed criterion has the greatest impact on achieving the main goal. The proposed universal set of criteria consists of five main criteria (technical, economic, social, environmental, and legal), under which twenty-eight detailed criteria are listed. The hybrid multi-criteria methodology was used to rank the proposed set of four wind farm locations in terms of chances for investment implementation in the shortest possible time. The ranking of the location obtained with this method should be treated as an element supporting the decision-maker. The location for wind power plant with installed capacity 40 MW was found to be the most suitable, and the results showed that the main contributing factors are carbon avoidance rate and the impact of the investment on environmentally protected areas. Full article

(This article belongs to the Special Issue Uncertainty Analysis and Risk Management in Power Systems and Electricity Markets)

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