In electricity markets, market rules such as price caps defer proper investment in generation. The resulting generation mix is therefore sub-optimal if compared to the one that maximizes social welfare. In this paper, an incentive mechanism for a price-capped multi-area energy only market is proposed. The market model is posed as a mixed complementarity problem using the optimality conditions of all the individual players that participate in the multi-area electricity market. The resulting equilibrium problem is solved using a decomposition approach based on the Alternating Direction Method of Multipliers. The proposed solution algorithm takes advantage of the multi-area structure of the problem and outperforms state-of-the-art practices for solving these types of problems.

With renewable energy sources becoming an ever-increasing share of the generation mix of modern power systems, having the proper amount of reserve becomes of utmost importance to ensure the short-term as well as the long-term adequacy level in the system. This reserve can be in the form of generation assets or it can be provided from assets on the demand side. The contribution that these resources make to the adequacy of the system is referred to as their capacity credit. This paper derives a methodology for calculating the capacity credit of a resource in a multi-area system. Based on that, an approach is developed that quantifies how strategic demand reserve should be distributed between power system areas in a multi-area system in order to reach individual long-term reliability targets in all areas. Lastly, an algorithm is derived that optimizes the coordinated procurement of multi-area strategic demand reserve by counterbalancing the value of lost load against the costs related to maintaining generation adequacy. A combination of an iterative multi-variate gradient approach and a Monte Carlo simulation with an efficient sensitivity analysis allows this to be achieved in a computationally economical way. An illustrative example and numerical simulations of test systems using real data from the Nordic power system demonstrate the effectiveness of the proposed approach.

The generation adequacy of electricity supply has been an ongoing concern since the restructuring of the industry. Ensuring generation adequacy was a rather straightforward task in the era of natural monopolies. Whose responsibility was it to ensure generation adequacy as the industry became deregulated and more fragmented? Who is willing to finance rarely used generating units? After decades of experience with the competitive electricity market, the question of whether market forces alone are sufficient to ensure generation adequacy still remains.

Recent energy policies have moreover set a goal of a high share of renewable energy in electricity markets. The presence of high levels of renewable generation makes the supply side of the market more uncertain. This volatility in energy production induces volatility in energy prices which means that the revenue stream of conventional generating technologies is more uncertain than it has traditionally been. This can even deteriorate the economics of some generators to the point where they exit the electricity market. The installed capacity of dispatchable generation can therefore be reduced.

These developments bring up the question of whether the generation adequacy of modern and future, deregulated and highly variable power systems is ensured. This dissertation focuses on modeling the generation adequacy of modern power systems with a high penetration of variable renewable energy sources. Moreover, the dissertation looks at some solutions with the aim of ensuring the generation adequacy of such systems through various means such as coordinated reserves, energy storage as well as utilizing the flexibility of the demand side of the market.

The models developed in this dissertation are verified using well-known test systems as well as through large-scale analysis of real-world systems. Aside from focusing on the simulation of power systems, the developed models moreover focus on achieving high computational efficiency. This is done through means such as advanced Monte Carlo simulation and optimization methods that apply decomposition to speed up the simulations.

Elförsörjningens leveranssäkerhet har varit en källa till oro alltsedan avregleringen av elmarknaden. Att säkerställa produktionens leveranssäkerhet var en ganska enkel uppgift i de tidigare naturliga monopolen. Vems ansvar var det att säkerställa produktionens leveranssäkerhet när industrin blev avreglerad och mer fragmenterad? Vem är villig att finansiera sällan använda produktionsenheter? Efter decennier av erfarenhet av den konkurrensutsatta elmarknaden kvarstår fortfarande frågan om marknadskrafterna är tillräckliga för att säkerställa produktionens leveranssäkerhet.

Energipolitiken i många länder har dessutom satt upp mål för en hög andel förnybar energi på elmarknaderna. Höga nivåer av förnybar produktion gör marknadens utbudssida mer osäker. Denna volatilitet i produktionen inducerar volatilitet i elpriser, vilket innebär att inkomsterna för konventionella produktionstekniker blir mer osäkra än tidigare. Detta kan försämra ekonomin hos vissa generatorer till den grad att de lämnar elmarknaden. Därför kan den installerade kapaciteten för kontrollerbar produktion reduceras.

Denna utveckling väcker frågan om i vilken utsträckning produktionens leveranssäkerhet kan säkerställas i framtida, avreglerade elkraftsystem med hög andel variabel elproduktion. Denna avhandling fokuserar på att modellera produktionens leveranssäkerhet för moderna kraftsystem med en hög andel variabla förnybara energikällor. Avhandlingen tittar dessutom på några lösningar i syfte att säkerställa leveranssäkerheten i sådana system genom olika medel som samordnade reserver, energilagring samt att utnyttja flexibilitet på marknadens efterfrågesida.

Modellerna som utvecklas i denna avhandling verifieras av välkända testsystem samt genom storskalig analys av befintliga kraftsystem. Förutom att fokusera på simulering av kraftsystem fokuserar de utvecklade modellerna dessutom på att uppnå hög beräkningseffektivitet. Detta görs genom medel som avancerad Monte Carlo-simulering och optimeringsmetoder som tillämpar dekomposition för att öka effektiviteten av simuleringarna.

This paper proposes a model of the behavior of an expected profit-maximizing merchant storage owner with the ability to exercise unilateral market power. The resulting non-linear bilevel optimization problem is transformed into a single-level stochastic bilinear program using the Karush-Kuhn-Tucker conditions of the lower-level Independent System Operator dispatch problem. By discretizing the offers and bids of the merchant storage owner, the problem is formulated as a stochastic disjunctive program. Using the disjunctive nature of the derived program, a specialized branch-and-bound algorithm that applies a linear quasi-relaxation of the merchant storage problem is proposed. Our solution algorithm is able to solve the problem in an efficient manner; returning the charge and discharge strategies for the merchant storage owner that yield the highest expected profits. Simulations of test systems reveal the various abilities of the merchant storage owner to exercise unilateral market power. Those include demand withholding, generation withholding and under-use which result in an increased congestion in both space and time when compared to the welfare-maximizing use of storage. Factors such as uncertain bids by other players, final state-of-charge requirements and arbitrage by other storage players are investigated. Moreover, numerical results demonstrate the superior computational performance of the proposed solution algorithm when benchmarked against current practices in the literature.

The integration of renewable energy sources, including wind power, in the adequacy assessment of electricity generation capacity becomes increasingly important as renewable energy generation increases in volume and replaces conventional power plants. The contribution of wind power to cover the electricity demand is less certain than conventional power sources; therefore, the capacity value of wind power is smaller than that of conventional plants.

This article presents an overview of the adequacy challenge, how wind power is handled in the regulation of capacity adequacy, and how wind power is treated in a selection of jurisdictions. The jurisdictions included in the overview are Sweden, Great Britain, France, Ireland, United States (PJM and ERCOT), Finland, Portugal, Spain, Norway, Denmark, Belgium, Germany, Italy and the Netherlands.

Generation adequacy is a concern in today's electricity market where intermittent renewable energy sources are rapidly becoming a greater share of the generation mix. This paper focuses on the North-European power system that is comprised of the system areas of the Nord Pool spot market. Sequential Monte Carlo Simulation is applied to assess the generation adequacy of this multi-area system for several future scenarios defined within the Nordic Flex4RES project. The paper gives insights into the characteristics of these adequacy problems that the system could face in a more sustainable future and quantifies their magnitude. Finally, some solutions based on the demand flexibility of residential electric heating are discussed.

Generation adequacy is a concern in today's electricity market where intermittent renewable energy sources are rapidly becoming a greater share of the generation mix. This study focuses on the Nordic and Baltic power system that is comprised of the system areas of the Nord Pool spot market. Sequential Monte Carlo Simulation is applied to assess the generation adequacy of this multi-area system for several future case studies, based on scenarios defined within the Nordic Flex4RES project. The report gives insights into the characteristics of these adequacy problems that the system could face in a more sustainable future, quantifies their magnitude and presents their characteristics. Finally, a solution based on the demand flexibility of residential electric heating is discussed, as a way to counter capacity deficit problems.

There is growing concern regarding generation adequacy within the power system industry. The ever-increasing injection of intermittent renewable resources makes it harder than before to estimate the reliability of modern power systems using traditional approaches. This paper develops a framework for estimating the reliability of modern power systems that have considerable levels of wind power generation. Monte Carlo simulation is applied using a very efficient importance sampling technique based on the cross-entropy method as well as the Copula theory. Tailor-made importance sampling functions for conventional generation, load, and wind power generation drastically reduce the number of samples required to estimate reliability parameters of interest. The methodology enables simulation of multi-area power systems with considerable amount of correlated wind power generation in each of the different areas. Simulation results confirm the efficiency as well as the accuracy of the proposed method and show that it is orders of magnitude faster than crude Monte Carlo simulation.

A generator's contribution to the generation adequacy of a power system is more accurately captured by its capacity credit than by its installed capacity. The capacity credit takes into account factors such as forced outages and limited energy supply and the latter is especially important for volatile renewable sources that behave quite differently from dispatchable sources. Their installed capacity gives very limited information about their contribution to the generation system adequacy. Traditional approaches for calculating the capacity credit treat the power system as a single area and calculate an aggregate value for the whole system. In this paper, a multi-area approach is introduced which is able to quantify how the capacity credit is distributed between different power system areas. A combination of an iterative multi-variate Newton approach and a Monte Carlo simulation with an efficient sensitivity analysis allows this to be achieved in a computationally economical way.

This paper presents an improved way of applying Monte Carlo simulation using the Cross-Entropy method to calculate the risk of capacity deficit of a composite power system. By applying importance sampling for load states in addition to generation and transmission states in a systematic manner, the proposed method is many orders of magnitude more efficient than crude Monte Carlo simulation and considerably more efficient than other Cross-Entropy based algorithms that apply other ways of estimating the importance sampling distributions. An effective performance metric of system states is applied in order to find optimal importance sampling distributions during pre-simulation that significantly reduces the required computational effort. Simulations, using well known IEEE reliability test systems, show that even problems that are nearly intractable using crude Monte Carlo simulation become very manageable using the proposed method.