This doctoral dissertation considers the Russian capacity market and CRMs in the post-liberalization period, where electricity and capacity prices are defined in a competitive way by the market. The research addresses the outcomes of having a capacity market and CRMs in Russia in the context of the energy trilemma (see Figure 12), where the balance should be the best option. On the one hand, CRMs in Russia are intended to provide energy security by ensuring capacity adequacy in the future. Furthermore, CRMs are chosen to be a support tool for renewable energy to achieve the sustainability goals and diversification of electricity generation. On the other hand, the question remains: is having a capacity market and CRMs an affordable choice? Thus, the trilemma framework is a viable way to define the questions and approaches taken in this research, as it examines the capacity market from different dimensions.
Energy Trilemma
Energy Affordability:
Impact of capacity market and CRMs on
consumer electricity cost
(Publication I-III)
Sustainability:
Analysis of the CRM for renewable
energy development and emission reduction (Publications II &III) Energy Security:
Effectiveness of capacity market and
CRMs in providing energy security (Publication: III-V)
Figure 12. Research design in the context of the energy trilemma.
The five publications included in the dissertation provide an analysis of the three dimensions of the trilemma. Publications I–III focus on the final consumer cost, which includes the capacity and CRMs costs, answering the energy affordability question.
Publications II and III discuss Russia’s environmental and renewable policies and the contribution of the CRMs to achieving the set sustainability goals. Publications I–V assess the capacity market from the perspective of ensuring capacity adequacy in the short term and long term.
3.1
Research questions and objectivesCapacity markets have been established to resolve the market failures connected to the resource adequacy. According to (Briggs & Kleit, 2013), the need for a capacity market results mainly from the inelasticity of demand and the presence of price caps in the energy-only market, set by the regulators. When the capacity market alone cannot provide the required resource adequacy, capacity subsidies (CRMs) are introduced in order to ensure it, as it has happened in Russia, the PJM market, and the UK (Gore, et al., 2012) (Briggs & Kleit, 2013) (Harbord & Pagnozzi, 2014). Capacity subsidies are usually organized at the expense of power consumers; however, the extent of their expenses has not been analysed for the electricity and capacity market in Russia. Moreover, there are other consequences associated with the implementation of a particular capacity market and CRMs, such as cross-border trade issues, capacity oversupply, and the use of capacity subsidies for the renewable energy support.
The main objective of this dissertation is to analyse the capacity market and CRMs in Russia to draw conclusions on its outcomes from different angles, such as ensuring resource adequacy, final consumer electricity cost, renewable energy development, and impacts on cross-border trade. The main research question is: What are the outcomes of the Russian capacity market and introduction of CRMs, where each outcome requires individual consideration? The question is complex and could be addressed from the perspective of the dimensions of the energy trilemma.
Each publication included in this doctoral dissertation answers a research question of its own, supplementing the main research question:
Publication I and II: Does a high capacity cost provide incentives for large industries to leave the capacity market?
Publication III: What are the contributions of a renewable support scheme to achieving a sustainability goal and to the electricity cost paid by a final consumer?
Publication IV: From the perspective of cross-border trade, what are the consequences of having an energy-plus-capacity market?
Publication V: Is there an opportunity for a new power plant to enter the market without subsidies?
This topic became relevant to academic and political discussions in the last decade. This is explained by the fact that many developed and developing countries are forced to consider a capacity market in order to ensure resource adequacy in the future. Regardless of the reasons behind implementation, the capacity market has similar challenges and issues as discussed above. An analysis of the Russian case could provide general lessons for other countries, as it is one of the first cases in the history of introducing a capacity market.
3.2
Research approach and methodsFour publications included in this dissertation apply a linear optimization approach, while Publication II employs a cost-benefit analysis (CBA). The analysis tool is commonly used for investment decision-making in energy management and planning (Meeus, et al., 2013). Publication II analyses the costs and benefits of the power production from APG in two cases, based on the access to the network, and defines the profitability of investing in a power plant for oil producers.
The linear optimization approach or linear programming (LP) is widely used for energy system modelling and for solving a variety of problems related to energy system operation (Zeng, et al., 2011). There are well known input-output dynamic market models based on linear programming for the system analysis such as MARKAL, EFOM, and TIMES (Bhattacharyya, 2011). They simulate system operation and identify the optimal configuration of the system that would ensure a minimum cost supply to meet the demand.
The LP method is also often used to solve some specific problems addressing energy efficiency issues, such as renewable energy sources and storage integration, and emission reduction. Zhou and Ang use LP models to measure economy-wide energy efficiency performance (Zhou & Ang, 2008). The methodology is used to solve the dispatch problem, especially in the case of integration of intermitted renewable power sources (Wang, et al., 2015). The primary fuel cost plays a crucial role in the formation of the total system cost, and it should be taken into account in electricity market models.
Application of LP models for the electricity market dispatch and pricing has evolved over the past few decades. For instance, (Vespucci, et al., 2013) and (Chernenko, 2012) apply LP models to estimate hourly market prices in Italy and Russia, respectively. They define an optimum market price by minimizing the total cost of the electricity in a price zone, assuming perfect competition. The main constraints in a modelling approach of this kind are the equality of supply and demand in one price zone and transmission constraints between price zones.
Usually, LP models have an objective to minimize the costs or maximize the profit or welfare of the market participants, which are subject to satisfying the constraints. In the general form, the model can be written as:
max: 𝐹(𝑋), (3.1)
𝑆. 𝑡. 𝐴𝑋 ≤ 0 (3.2)
𝑋 ≥ 0 (3.3)
where 𝑋 is a vector of decision variables of the linear objective function F, and the matrix A indicates the economic, operational, or regulative constraints.
Publication I applies a single objective function to minimize the total electricity cost of an industrial power consumer. The decision variable in the case is the power production of an on-site distributed generator, which is constrained by the regulative requirement to be less than 25 MW. In Publication IV, the total welfare from cross-border trade is maximized for both interconnected markets for various transmission allocation schemes and capacity market arrangements.
Publications III and V use linear optimization to model DAM electricity prices with an assumption of perfect competition, that is, assuming perfect information for all market participants and no strategic behaviour. A two-step linear optimization model is developed in Publication V, where two markets (electricity and capacity) are modelled separately. Step one, the electricity market model, defines market-clearing prices for a point of the load duration curve in order to estimate the profit of the power producers from the electricity market. Then, in step two, a capacity auction price is estimated to decide upon the opportunity of a new generation entry.
3.3
Research dataThe data used in the research are gathered from the official websites and open access reports of companies or organizations. A detailed list of the sources is given in Table 2.
The currency exchange rates and capital costs for power plants were taken for the year when the research was conducted. Therefore, some price references may deviate considerably as result of weakening of the Russian rouble in 2014 (Milov, 2015).
Data on power producers in Russia were collected for Publication V from various sources such as the official web site of the generation companies and databases such as the Energy Base (Energy Base, 2016) for the year 2015.
Table 2. Main sources of research data.
N Source Description Data
1 http://www.atsenergo.ru/ Administrator of Trading System of Russia
Electricity prices, electricity demand
2 http://monitor.so-ups.ru/ Competitive Capacity Auction
Results of the CCA, capacity demand
3 http://so-ups.ru/ System Operator of Russia Installed capacities, types of power technologies
4 http://www.np-sr.ru/ Market Council Regulatory documents, installed capacities under CRMs
5 http://www.consultant.ru/ Legal document source Government Decrees, Resolutions, and Federal Laws 6 http://www.fstrf.ru/tariffs Federal Tariff Service4 Electricity and transmission
tariffs
7 http://www.gazprom.com/ JSK Gazprom Natural gas tariffs for the domestic market
8 http://www.nordpoolspot.com/ Nord Pool Electricity price in the Nordic countries
9 http://www.stat.fi/ Finnish statistic Energy and fuel prices in Finland 10 http://energia.fi/ Finnish Energy Finnish electricity sector
technology mix and demand
3.4
Limitations of the researchThe major limitation of conducting research on the Russian electricity and capacity market is associated with data accuracy and sources. For instance, not all the power production companies provide detailed data on each power plant efficiency and installed capacity. Instead, companies tend to give aggregated data on their production assets. The official reports of market participants are sometimes not consistent, or the data provided in one year may be absent in the next. Such behaviour is connected to the constant amendments to the Decree No. 24 on information disclosure by the electricity and capacity market participants (Government of the Russian Federation, 2004).
Publications I and II consider hypothetical cases of distributed generation implementation by large power consumers and refer to average electricity and capacity prices. However, electricity price is volatile and highly dependent on fuel prices. The oil field depletion rate in Publication II was assumed based on the literature review on oil production technology, yet the depletion rate can vary significantly for different oil fields. Therefore, the results can vary considerably depending on the case, and a separate CBA should be made for each case.
4 The Federal Antimonopoly Service took over the responsibilities of the Federal Tariff Service in 2015
The analysis in Publication III considers a case where all the capacity under the CRM-RES is constructed. However, the results of competitive bidding for CRM-RES show that not all of the supported capacity is going to be constructed, and therefore, the impact of the support scheme will be lower than the results shown in the publication. The locations of the renewable power plants under capacity support were assumed based on the renewable power source map, because at the time of the research competitive bidding had not been implemented.
The welfare calculations in Publication IV were based on supply curves calibrated by the authors; these curves, again, are subject to data collected from different sources. Thus, more accurate data would provide more accurate numerical results. However, the same conclusion would most likely be drawn.
An assumption of perfect competition in the electricity and capacity market is made in Publication V; it is a rather theoretic approach, yet widely used in the literature. Such an assumption is possible in cases where market participants have no market power.
According to Chernenko (2015), despite the high market concentration there was no sign of market power abuse in the Russian electricity and capacity market.
The impact of cross-subsidization on the electricity and capacity market prices, mentioned as a challenge of the current market organization in Russia, is not taken into account in this doctoral dissertation. Because of the difficulties associated with the non-transparency of cross-subsidization allocation to the final consumer cost, it is not addressed in more detail in this research.