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• The paper provides review of models integrating RES in Generation Expansion Planning. • It classifies models based on the applied theoretical approach in three categories. • Optimization, general/partial equilibrium and alternative models. • It compares their characteristics, advantages/disadvantages and their suitability. • It contributes in better understanding on the expected outcomes of each methodology. The Generation Expansion Planning (GEP) stands as one as one of the most discussed topics within the academia and decision makers in the energy sector, especially related to meeting deep emission reduction targets. Every country, aiming at decarbonizing its economy, focuses on the application of policies that could enhance the penetration of Renewable Energy Sources (RES) in its power capacity mix. GEP is a complex task, combining techno-economic, financial, spatial and environmental characteristics. Several models are developed to model GEP, applying different methodological approaches. The underlying theory is very important as it might inherit bias in the resulted outcomes. The debate on the appropriateness of each methodology is increased, especially as projected outlooks deviate from reality. The paper aims to provide a review of the models employed to integrate RES in the GEP. The paper classifies models in three generic categories: optimisation models, general/partial equilibrium models and alternative models, not adopting the optimum integration of RES in the GEP. It provides insights on the characteristics, advantages and disadvantages of the theoretical approaches implemented, as well on their suitability for different aspects of the problem, contributing in the better understanding on the expected outcomes of each methodology. [ABSTRACT FROM AUTHOR]

The increasing share of intermittent Renewable Energy Sources (iRES) in the energy mix leads to a growing need for their accurate representation in energy system models. In this paper, we analyze how the results of generation planning models are impacted by the spatial representation used to model intermittent renewable technologies. It is shown that the spatial resolution, and to a lesser extent the spatial aggregation method used in a planning model, have a significant influence on the obtained system cost. The effect is quantified in a case study of European geographical scale, by varying the spatial resolution from highly resolved (hundreds of sites) to highly aggregated (1 site). A resolution-dependency of the system cost is observed under multiple renewable generation targets, and different renewable capacity restrictions. In doing so, we highlight the importance of implementing an appropriate capacity limit, especially when a high spatial resolution is used. Additionally, basic spatial aggregation methods -to process high resolution input data- are compared. • Isolation of renewable representation effects in a stylized GEP model. • iRES resolution has a strong impact on GEP model results for Europe. • Capacity limits play an important role in this effect, especially in high resolution. • Spatial aggregation method only impacts models with low resolutions. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framework for operational flexibility assessment in power system planning with a significant share of intermittent renewable energy sources (RES). The framework proposed includes: (i) the formulation of an integrated generation expansion planning (GEP) and unit commitment (UC) model accounting for key short-term technical constraints, (ii) the elaboration of accurate and systematic horizon reduction methods to alleviate the computational burden of the resulting large-sized optimization problems and (iii) the definition of suitable metrics for the operational flexibility assessment of the obtained plans. The framework is applied to a ten year planning horizon of a realistically sized case study representing the national power system of France, under several scenarios of RES penetration levels and carbon limits, spanning levels of up to 50%. The importance of incorporating the detailed short-term constraints within long-term planning models is shown. The results of the assessment show that, under high renewable energy penetration, neglecting the short-term constraints may lead to plans significantly short on flexibility, reaching shortage levels of up to 50% in frequency and several GWs in magnitude. Also, the load not served reaches levels of up to 3% and carbon emission is underestimated by up to 60%. Furthermore, the results highlight the importance of relying on suitable quantitative metrics for operational flexibility assessment in power systems planning rather than solely relying on generic performance measures, such as system costs and mixes of power plants, which are shown not to sufficiently reflect the flexibility levels of the obtained plans. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the novel long-term energy mix optimization model eMix , which takes into account daily requirements of power system operation (primary, secondary and tertiary reserve) and how should they react to increasing RES penetration. Until now there has been no computational effective, long-term optimization model of energy mix with individual representation of power generating assets that allows for RES development incorporation and consider the daily requirement of power system operation. Existing unit commitment models, although comprehensive, are not appropriate for long-term, large-scale problems because of computational limitations. The eMix links long-term planning (investments) with short-term requirements of power system operation reserves (primary, secondary, tertiary) provided by dispatchable units. Additionally, development of the RES introduces stronger requirements to the reserve’s level in order to maintain the flexibility of the power system operation. This relation has significant impact on the final energy mix and was modeled in the eMix . The costs are calculated annually for each individual unit (given to the application of Mixed Integer Linear Programming), to reflect the real conditions of energy sector operation, where annual repayment of loans, amortization as well as operation and maintenance costs are applied. eMix is also fully scalable and allows for freely definable interval and horizon of the calculation. To prove the quality of presented model, the authors calibrated it with the data from Polish power system for different scenarios. Results indicate that the consideration of financial costs and required reserves increase the reliability of the optimization outcomes. [ABSTRACT FROM AUTHOR]

This paper presents a multistage adaptive robust generation expansion planning model, which accounts for short-term unit commitment and ramping constraints, considers multi-period and multi-regional planning, and maintains the integer representation of generation units. The uncertainty of electricity demand and renewable power generation is taken into account through bounded intervals, with parameters that permit control over the level of conservatism of the solution. The multistage robust optimization model allows the sequential representation of uncertainty realization as they are revealed over time. It also guarantees the non-anticipativity of future uncertainty realizations at the time of decision-making, which is the case in practical real-world applications, as opposed to two-stage robust and stochastic models. To render the resulting multistage robust problem tractable, decision rules are employed to cast the uncertainty-based model into an equivalent mixed integer linear (MILP) problem. The re-formulated MILP problem, while tractable, is computationally prohibitive even for moderately sized systems. We, thus, propose a solution method relying on the reduction of the information basis of the decision rules employed in the model, and validate its adequacy to efficiently solve the problem. The importance of considering multistage robust frameworks for accounting for net-load uncertainties in generation expansion planning is illustrated, particularly under a high share of renewable energy penetration. A number of renewable penetration scenarios and uncertainty levels are considered for a case study covering future generation expansion planning in Europe. The results confirm the effectiveness of the proposed approach in coping with multifold operational uncertainties and for deriving adequate generation investment decisions. Moreover, the quality of the solutions obtained and the computational performance of the proposed solution method is shown to be suitable for practical policy-making generation expansion planning problems, seeking to evaluate the impact of uncertainty on future system-wide performance. • A multistage adaptive robust optimization model for generation expansion planning. • Key operational uncertainties due to large shares of renewable energy are considered. • Novel solution method based on the reduction of information basis for decision rules. • Significant reduction in calculation time for solving the multistage robust model. • Practical relevance of the proposed approach for system planning under uncertainties. [ABSTRACT FROM AUTHOR]

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors' forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms' heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm's own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent. [Display omitted] • A new agent-based model linking electricity sector investments and markets. • Agents are risk-averse, adaptive, heterogeneous firms with imperfect information. • Results compared against traditional perfect competition capacity expansion. • Model captures realistic suboptimalities, capacity scarcities and recourse actions. • Outcomes depend not only on firm's own parameters but also on other firms' actions. [ABSTRACT FROM AUTHOR]

Abstract: We present a bilevel optimization approach to designing effective and efficient incentive policies for stimulating investment in renewable energy. The effectiveness of an incentive policy is its capability to achieve a goal that would not be achievable without it. Renewable portfolio standards are used in this paper as the policy goal. The efficiency of an incentive policy is measured by the amount of policy intervention, such as taxes collected or subsidies paid, to achieve the policy goal. We obtain the most effective and efficient incentive policies in the context of generation expansion planning, in which a centralized planner makes investment decisions for the energy system to serve projected demand of electricity. A case study is conducted on integrated coal transportation and electricity transmission networks representing the contiguous United States. The numerical analysis from the case study provides insights on the comparison of various incentive policies. The sensitivity of the incentive policies with respect to coal production cost, wind energy investment cost, and transmission capacity is also studied. [Copyright &y& Elsevier]

• Not considering alternative sources of flexibility can lead to significant errors. • Unit commitment constraints have limited impact with alternative flexibility sources. • Unit commitment constraints mainly impact investments in storage technologies. • Sensitivity to assumed flexibility of power plants and reserve requirements is high. • Errors due to approximate formulations are small compared to the sensitivities. In the context of an increasing penetration of intermittent renewable energy sources, the impact of neglecting unit commitment constraints in generation expansion planning models has been widely assessed and demonstrated in the literature. However, the focus is often on thermal generators being the only source of flexibility, and the sensitivity to certain assumptions (e.g., system requirements and technical characteristics) has not been investigated. This paper contributes to the existing literature by revisiting the relevance of considering unit commitment constraints in generation expansion planning models for varying assumptions regarding both the available flexibility and the need for operating reserve requirements. The results indicate that if storage technologies are considered, integrating technical constraints has only a minor impact on both overall cost projections and most investments, with the exception of investments in storage technologies themselves. Furthermore, the investments in storage technologies are shown to be highly sensitive to the assumptions made regarding the assumed flexibility of thermal generators and the future need for operating reserves. These sensitivities are shown to be of an order of magnitude higher or of an equal order of magnitude compared to the impact of integrating technical constraints with continuous rather than integer commitment variables. [ABSTRACT FROM AUTHOR]