Impacts of variable renewable energy on wholesale markets and generating assets in the United States: A review of expectations and evidence

We synthesize available literature, data, and analysis on the degree to which growth in variable renewable energy (VRE) has impacted or might in the future impact bulk power system assets, pricing, and costs in the United States. Most studies of future scenarios indicate that VRE reduces wholesale energy prices and capacity factors of thermal generators. Traditional baseload generators are more exposed to these changing market conditions than low-capital cost and more flexible intermediate and peak-load generators. From analysis of historical data we find that VRE is already influencing the bulk power market through changes in temporal and geographic patterns areas with higher levels of VRE. The most significant observed impacts have concentrated in areas with significant VRE and/or nuclear generation along with limited transmission, with negative pricing also often occurring during periods with lower system-wide load. So far, however, VRE, has had a relatively modest impact on historical average annual wholesale prices across entire market regions, at least in comparison to other drivers. The reduction of natural gas prices is the primary contributor to the decline in wholesale prices since 2008. Similarly, VRE impacts on thermal plant retirements have been limited and there is little relationship between the location of recent retirements and VRE penetration levels. Although impacts on wholesale prices have been modest so far, impacts of VRE on the electricity market will be more significant under higher VRE penetrations

Long Term Planning in Restructured power Systems: Dynamic Modelling of Investments on New Power Generation under Uncertainty

This thesis describes the development of three decision support models for long-term investment planning in restructured power systems. The model concepts address the changing conditions for the electric power industry, with the introduction of more competitive markets, higher uncertainty and less centralised planning. Under these circumstances there is an emerging need for new planning models, also for analyses of the power system in a long-term perspective. The thesis focuses particularly on how dynamic and stochastic modelling can contribute to the improvement of decision making in a restructured power industry. We argue that the use of such modelling approaches has become more important after the introduction of competitive power markets, due to the participants’ increased exposure to price fluctuations and economic risk. Our models can be applied by individual participants in the power system to evaluate investment projects for new power generation capacity. The models can also serve as a decision support tool on a regulatory level, providing analyses of the long-term performance of the power system under different regulations and market designs. In Chapter 1, we give a brief introduction to the ongoing development towards restructuring and liberalisation of the electrical power system. A discussion of the operation and organisation of restructured power systems is also provided. In Chapter 2, we look more specifically at different modelling approaches for expansion planning in electrical power systems. We also discuss how the contributions in this thesis compare to previous work in the field of decision support models for long-term planning in both regulated and competitive power systems. In Chapter 3, we develop a power market simulation model based on system dynamics. The advantages and limitations of using descriptive system dynamics models for long-term planning purposes in this context are also discussed. Chapter 4 is devoted to a novel optimisation model which calculates the optimal investment strategy for a profit maximising investor considering investments in new power generation capacity. The model is based on real options theory, which is an alternative to static discounted cash flow evaluations of investments projects under uncertainty. In the model we represent load growth as a stochastic variable. A stochastic dynamic programming algorithm is applied in order to solve the investment problem. Prices and profits are calculated in a separate model, whose parameters can be estimated based on historical data for load, prices and installed capacity in the power system. In Chapter 5, we extend the stochastic dynamic optimisation model from Chapter 4, so that the investor now can choose between two different power generation technologies to invest in. An alternative representation of the power market is also implemented, which makes it possible to use either a profit or a social welfare objective in the optimisation. With this model we can compare the optimal investment decisions, and the dynamics of investments, prices and reliability, which follow from centralised and decentralised decision making. The main scientific contributions in the thesis lie in the combined use of economic theory for restructured power systems and theory for optimal investments under uncertainty. With an explicit representation of the power market, the dynamic investment models can identify profit maximising investment strategies under different regulations and market designs. The use of physical state variables in the models also facilitates analyses of the long-term consequences for the power system, which result from the optimal decentralised investment decisions. Decision support models for expansion planning in the regulated power industry do not address the aspect of competition and decentralised decision making. At the same time, long-term uncertainties and their impact on optimal investment decisions are rarely represented in planning models for the competitive industry. The stochastic dynamic models in this thesis therefore provide a new framework for long-term analysis of investments and prices in restructured power systems. Potential applications of the investment models are demonstrated in a number of illustrative examples in the thesis. Through the analyses in these examples we have gained increased insight into the complex dynamics of prices, investments and security of supply in competitive power systems.PhD i informasjons- og kommunikasjonsteknologiPhD in Information and Communications Technolog

Operational Valuation for Energy Storage under Multi-stage Price Uncertainties

This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City

Multi-Stage Decision Rules for Power Generation & Storage Investments with Performance Guarantees

We develop multi-stage linear decision rules (LDRs) for dynamic power system generation and energy storage investment planning under uncertainty and propose their chance-constrained optimization with performance guarantees. First, the optimized LDRs guarantee operational and carbon policy feasibility of the resulting dynamic investment plan even when the planning uncertainty distribution is ambiguous. Second, the optimized LDRs internalize the tolerance of the system planner towards the stochasticity (variance) of uncertain investment outcomes. They can eventually produce a quasi-deterministic investment plan, which is insensitive to uncertainty (as in deterministic planning) but robust to its realizations (as in stochastic planning). Last, we certify the performance of the optimized LDRs with the bound on their sub-optimality due to their linear functional form. Using this bound, we guarantee that the preference of LDRs over less restrictive -- yet poorly scalable -- scenario-based optimization does not lead to financial losses exceeding this bound. We use a testbed of the U.S. Southeast power system to reveal the trade-offs between the cost, stochasticity, and feasibility of LDR-based investments. We also conclude that the LDR sub-optimality depends on the amount of uncertainty and the tightness of chance constraints on operational, investment and policy variables

LASSO vector autoregression structures for very short-term wind power forecasting

The deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniques that take advantage of near real-time measurements collected from geographically distributed sensors. This paper describes a forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model using the Least Absolute Shrinkage and Selection Operator (LASSO) framework. The alternating direction method of multipliers is applied to fit the different VAR-LASSO variants and create a scalable forecasting method supported by parallel computing and fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66 wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. The proposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse-VAR model from the state of the art.LASSO Vector Autoregression Structures for Very Short-term Wind Power Forecastin