Long-term Capacity Investment Research Articles

Analyzing the impact of demand response and reserves in islands energy planning

Small Islands usually rely on fossil fuels for their energy supply and face common challenges such as high energy costs and carbon dioxide emissions. For these reasons they represent interesting cases for analysing the transition towards a clean and secure energy system. Nevertheless, integrating non-dispatchable Renewable Energy Sources in the power grid causes stability issues and this is particularly true for island grids. Such issue is not fully considered in long-term energy planning; indeed, an important factor that should be considered in order to ensure the reliability of the grid are Reserves. There are different types of Reserves depending on the reactiveness/response time and the duration of the service. In this paper, primary and secondary reserves have been analysed in order to plan the long-term energy transition of the small island of Favignana, Italy by means of the new version of H2RES, a Linear Programming single-objective optimisation model able to provide a long-term capacity investment and dispatching optimisation. It has been found that biomass generators are favoured to both photovoltaic and wind turbines for their ability to provide reserves and also decrease the unpredictability of the supply. Batteries and Electrolysers are also used mostly for reserve provision.

Optimal competitive capacity strategies: Evidence from the container shipping market

For nearly two decades, ocean carriers have been locked in an arms race for capacity, which has led to huge losses for many and even bankruptcy for some. We investigate the nature of this investment race by studying a long-term capacity investment problem in a duopoly under demand uncertainty. In our model, two firms make sequential capacity decisions, responding to each other’s current and future capacity. We consider two types of strategies which differ in terms of how a firm considers the opponent’s future capacity in its own strategy: a proactive strategy where the firm assumes that the opponent will respond using a certain strategy, or a reactive strategy where the firm assumes that the opponent’s future capacity remains unchanged. In the proactive case, we allow the firm to have different assumptions on the opponent’s strategy, representing different amounts of information the firm has on the opponent. For each type of strategies, we derive the firm’s optimal decisions on both the timing and size of capacity adjustments, specified by an array of intervals for the optimal capacity in a given capacity space in each period. Using detailed data from the container shipping market (2000–2015), we illustrate how to plan competitive capacity investments, following our model. By comparing the optimal decisions specified by our model with the reality, we show that the realized capacity decisions of the leading carriers, which were often questioned as irrational, are close to optimal, assuming these carriers follow proactive strategies. By revealing the underlying structures of different strategies, that is, the stayput intervals, we show how a specific strategy brings value to firms under competition. Based on our results, we provide practical guidelines to carriers and firms which operate in a similar competitive market for implementing an effective competitive capacity strategy.

A long-term capacity investment and operational energy planning model with power-to-X and flexibility technologies

In this research, we present a new long-term energy planning model that considers endogenous capacity investment, energy dispatch, Power-to-X, and demand response technologies. A thorough literature review of existing energy planning models is also presented, allowing to present the distinctive characteristics of the proposed model. The proposed model considers an energy system with the objective of minimizing the total capacity investment cost, throughout all technologies, and the operational cost faced by the system in satisfying energy demand. The model also considers the links among different demand sectors, including the links between the electricity, industry, heat, transport, and electro-fuels (e.g., Hydrogen) sectors. The proposed model is used to study the decarbonization of the Croatian energy system under distinct policies associated to RES levels and CO2 emissions goals. We demonstrate that Power-to-X technologies can certainly provide the flexibility that is required by new capacity investments in variable renewable energy sources, obtaining systems with lesser levels of critical excess of energy production. Higher usage of battery storage and Power-to-heat technologies are adopted primarily for variable renewable shares and CO2 reductions of close to 80%, while below such levels, the adoption of such technologies is limited. Additionally, Power-to-heat flexibility options become the major technologies when limits on CO2 emissions from the heating sector are imposed and, particularly, when the variable renewable energy shares in the electricity sector gets close to levels of 60%.

Technical Note—A Near-Optimal Algorithm for Real-Time Order Acceptance: An Application in Postacute Healthcare Services

A Near-Optimal Patient Admission Policy in Postacute Care Motivated by applications in the postacute healthcare industry, in “Technical Note—A Near-Optimal Algorithm for Real-Time Order Acceptance: An Application in Postacute Healthcare Services,” Qu, Dawande, and Janakiraman study an infinite-horizon, stochastic optimization problem with a set of long-term capacity investment decisions and a sequence of real-time order acceptance/rejection decisions. To maximize the long-run average expected profit per period, the firm accepts/rejects stochastically arriving referrals in real time. Referrals differ in the revenue they offer to the firm, the resource requirements and the frequency of usage of the resources, and the stochastic duration of the episode. The authors develop a simple policy, derive a worst case guarantee on its optimality gap, and show that the policy is asymptotically optimal. They also illustrate an impressive numerical performance of the policy using public healthcare data.

System adequacy in the Southern African Power Pool: A case for capacity mechanisms

The requirement for increased investment in electrical energy infrastructure in the Southern African Power Pool (SAPP) region is contextualised. Background on the SAPP is provided for reference. A brief assessment of historical capacity adequacy in the SAPP region shows historically sustained levels of inadequacy as well as distinctive investment cycles primarily as a result of administratively determined generation capacity investments within each member country. The introduction of an appropriately designed capacity mechanism is proposed for SAPP to facilitate system adequacy and incentivise long-term capacity investment.

The structural impact of renewable portfolio standards and feed-in tariffs on electricity markets

The capacity of renewable energy sources (RES) has grown rapidly worldwide, and this growth has benefited from such support schemes as renewable portfolio standards (RPS), feed-in tariffs (FITs), and market premia (MP). Previous research concentrated on comparing the effectiveness of these policy instruments at driving RES investment, but the field’s focus has shifted toward evaluating how they structurally affect electricity markets. In particular, research has sought to assess how much RES support schemes contribute to achieving the three main objectives of electricity policy, the affordability, reliability, and sustainability of electricity supply. In this work, we quantitatively compare RPS, FIT, and MP schemes in terms of those criteria by simulating the impact of all three support schemes via a dynamic long-term capacity investment model. We find that each support scheme increases RES penetration and thereby reduces carbon dioxide (CO2) emissions. Whereas MP and FITs can achieve this outcome at lower cost, RPS delivers more robust results.

Competitive Capacity Investment Under Uncertainty

We consider a long-term capacity investment problem in a competitive market under demand uncertainty. Two firms move sequentially in the competition and a firm’s capacity decision interacts with the other firm’s current and future capacity. Throughout the investment race, a firm can either choose to plan its investments proactively, taking into account possible responses from the other firm, or decide to respond reactively to the competition. In both cases, the optimal decision at each period is determined according to an ISD (Invest, Stayput, Disinvest) policy. We develop two algorithms to efficiently derive proactive ISD policies for the leader and follower firms. Using data from the container shipping market (2000-2015), we show that the optimal capacity determined by our competitive strategy is consistent with the realized investments in practice. By revealing strategical flexibility of proactive strategies, our results demonstrate that firms in the competition can gain more capacity and profit through such a strategy. Using Monte Carlo simulations, we explore the impact of different market conditions and investment irreversibility levels on capacity strategies. In particular, by comparing the results of competitive strategies and strategies that separate firms into different markets, we show that both firms can benefit from the competition and that market downturns likely lead to investment cascades.

Short-term market power assessment in a long-term dynamic modeling of capacity investment

This paper proposes a dynamic time simulation model for long-term generation capacity investment decisions in the presence of either perfect or imperfect electricity market. The model is based on system dynamics concept in which the dynamics of capacity construction is traced using the forecast of electricity price cleared in the short-term electricity market. Both the perfect and oligopolistic competitions are considered and a market power index is defined to evaluate the competition level of the electricity market. The short-term and long-term dynamic analysis are used to represent the generation firms' behavior in bidding strategy and capacity investment, respectively. Both possibilities of using forward bilateral contracts and application of wind power generation have been assessed individually in the proposed model in order to mitigate the market power. The time simulation results derived from a case study demonstrate the effectiveness of the model in illustrating the oligopoly in electricity marketplace and also the alternative decisions to attenuate it. Such a decision tool enables both the generation firms and the regulators to gain perfect insight into the oligopoly and its consequences in the long-term capacity investment in the electricity market.

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.

Dynamic assessment of capacity investment in electricity market considering complementary capacity mechanisms

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market. Investment decisions are fundamentally based on total revenues gained by investors. In most electricity markets, the complementary mechanisms are designed to ensure a desired level of reliability while covering investment costs of the suppliers. In such an environment, investment decisions are highly sensitive to expectation of price signals in both of energy market and capacity mechanisms. In this work, the system dynamics concepts are used to model the structural characteristics of electricity market such as, long-term firms’ behavior and relationships between variables, feedbacks, and time delays by appropriately bundling the energy market and capacity mechanisms. The market oriented capacity price as well as non-competitive capacity payments and a proposed hybrid capacity mechanism are linked with the energy market in the model. Such a decision model enables both the generation companies and the regulators gaining perfect insights into the possible consequences of different decisions they make under different policies and market conditions. In order to examine the performance of the electricity market with different capacity mechanisms, a case study is presented which exhibits the effectiveness of the proposed model.