Generation expansion planning optimisation with renewable energy integration: A review

Generation expansion planning consists of finding the optimal long-term plan for the construction of new generation capacity subject to various economic and technical constraints. It usually involves solving a large-scale, non-linear discrete and dynamic optimisation problem in a highly constrained and uncertain environment. Traditional approaches to capacity planning have focused on achieving a least-cost plan. During the last two decades however, new paradigms for expansion planning have emerged that are driven by environmental and political factors. This has resulted in the formulation of multi-criteria approaches that enable power system planners to simultaneously consider multiple and conflicting objectives in the decision-making process. More recently, the increasing integration of intermittent renewable energy sources in the grid to sustain power system decarbonisation and energy security has introduced new challenges. Such a transition spawns new dynamics pertaining to the variability and uncertainty of these generation resources in determining the best mix. In addition to ensuring adequacy of generation capacity, it is essential to consider the operational characteristics of the generation sources in the planning process. In this paper, we first review the evolution of generation expansion planning techniques in the face of more stringent environmental policies and growing uncertainty. More importantly, we highlight the emerging challenges presented by the intermittent nature of some renewable energy sources. In particular, we discuss the power supply adequacy and operational flexibility issues introduced by variable renewable sources as well as the attempts made to address them. Finally, we identify important future research directions

Assessment of underlying capacity mechanism studies for Greece

The increased electricity production from variable sources in the EU combined with the overall decline in demand in recent years, have raised concerns about the security of electricity supply, in general, and in particular about generation adequacy and flexibility, prompting some Member States to consider new public interventions, the so-called capacity remuneration mechanisms. This work presents a review of the underlying capacity mechanism studies for Greece based on European best practices to highlight the latest developments and current trends.JRC.C.3-Energy Security, Distribution and Market

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

An agent-based simulator for quantifying the cost of uncertainty in production systems

Product-mix problems, where a range of products that generate different incomes compete for a limited set of production resources, are key to the success of many organisations. In their deterministic forms, these are simple optimisation problems; however, the consideration of stochasticity may turn them into analytically and/or computationally intractable problems. Thus, simulation becomes a powerful approach for providing efficient solutions to real-world productmix problems. In this paper, we develop a simulator for exploring the cost of uncertainty in these production systems using Petri nets and agent-based techniques. Specifically, we implement a stochastic version of Goldratt’s PQ problem that incorporates uncertainty in the volume and mix of customer demand. Through statistics, we derive regression models that link the net profit to the level of variability in the volume and mix. While the net profit decreases as uncertainty grows, we find that the system is able to effectively accommodate a certain level of variability when using a Drum-Buffer-Rope mechanism. In this regard, we reveal that the system is more robust to mix than to volume uncertainty. Later, we analyse the cost-benefit trade-off of uncertainty reduction, which has important implications for professionals. This analysis may help them optimise the profitability of investments. In this regard, we observe that mitigating volume uncertainty should be given higher consideration when the costs of reducing variability are low, while the efforts are best concentrated on alleviating mix uncertainty under high costs.This article was financially supported by the State Research Agency of the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/50110 0 011033), via the project SPUR, with grant ref. PID2020–117021GB-I00. In addition, the authors greatly appreciate the valuable and constructive feedback received from the Editorial team of this journal and two anonymous reviewers in the different stages of the review process

Nonparametric Bayes Modeling of Populations of Networks

Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model which reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance --- compared to state-of-the-art models --- in simulations and application to human brain networks

A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System