Fairness in smart grid congestion management

With the energy transition, grid congestion is increasingly becoming a problem. This paper proposes the implementation of fairness in congestion management by presenting quantitative fair optimization goals and fairness measuring tools. Furthermore, this paper presents a congestion management solution in the form of an egalitarian allocation mechanism. Finally, this paper proves that this mechanism is truthful, pareto efficient, and maximizes a fair optimization goal

Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability

The integration of large-scale renewable generation has major implications on the operation of power systems, two of which we address in this work. First, system operators have to deal with higher degrees of uncertainty due to forecast errors and variability in renewable energy production. Second, with abundant potential of renewable generation in remote locations, there is an increasing interest in the use of High Voltage Direct Current lines (HVDC) to increase transmission capacity. These HVDC transmission lines and the flexibility and controllability they offer must be incorporated effectively and safely into the system. In this work, we introduce an optimization tool that addresses both challenges by incorporating the full AC power flow equations, chance constraints to address the uncertainty of renewable infeed, modelling of point-to-point HVDC lines, and optimized corrective control policies to model the generator and HVDC response to uncertainty. The main contributions are twofold. First, we introduce a HVDC line model and the corresponding HVDC participation factors in a chance-constrained AC-OPF framework. Second, we modify an existing algorithm for solving the chance-constrained AC-OPF to allow for optimization of the generation and HVDC participation factors. Using realistic wind forecast data, for 10 and IEEE 39 bus systems with HVDC lines and wind farms, we show that our proposed OPF formulation achieves good in- and out-of-sample performance whereas not considering uncertainty leads to high constraint violation probabilities. In addition, we find that optimizing the participation factors reduces the cost of uncertainty significantly

Achieving Very High PV Penetration

This article argues that optimally deployed intermittency solutions could affordably transform solar power generation into the firm power delivery system modern economies require, thereby enabling very high solar penetration and the displacement conventional power generation. The optimal deployment of these high‐penetration enabling solutions imply the existence of a healthy power grid, and therefore imply a central role for utilities and grid operators. This article also argues that a value‐based electricity compensation mechanism, recognizing the multifaceted, penetration‐dependent value and cost of solar energy, and capable of shaping consumption patterns to optimally match resource and demand, would be an effective vehicle to enable high solar penetration and deliver affordable firm power generation

Improving the Market for Flexibility in the Electricity Sector. Report of a CEPS Task Force. CEPS Task Force Report

Electricity will play a greater role in the transport and building sectors and all decarbonisation scenarios point to the increasing electrification of the energy system. To reach EU climate change targets, however, electricity will need to come increasingly from low carbon sources, especially (but not only) from variable renewable energy sources. Both trends − the electrification of sectors and the need to integrate electricity from variable renewables − mean that the electricity sector should become more flexible. This report reflects the discussions held in the CEPS Energy Climate House Task Force on Creating a Market Design for Flexibility in EU Electricity Markets, which met between April and September 2017. The Task Force formulated a number of recommendations in the areas of short-term and balancing markets; grid reinforcement and cross-zonal capacity allocation; aggregation; priority dispatch; DSOs (distribution system operators); and sectoral integration

The Economics of Natural Gas Infrastructure Investments - Theory and Model-based Analysis for Europe

Changing supply structures, security of supply threats and efforts to eliminate bottlenecks and increase competition in the European gas market potentially warrant infrastructure investments. However, which investments are actually efficient is unclear. From a theoretical perspective, concepts from other sectors regarding the estimation of congestion cost and efficient investment can be applied - with some extensions - to natural gas markets. Investigations in a simple analytical framework, thereby, show that congestion does not necessarily imply that investment is efficient, and that there are multiple interdependencies between investments in different infrastructure elements (pipeline grid, gas storage, import terminals for liquefied natural gas (LNG)) which need to be considered in an applied analysis. Such interdependencies strengthen the case for a model-based analysis. An optimization model minimizing costs can illustrate the first-best solution with respect to investments in natural gas infrastructure; gas market characteristics such as temperature-dependent stochasticity of demand or the lumpiness of investments can be included. Scenario analyses help to show the effects of changing the underlying model presumption. Hence, results are projections subject to data and model assumption - and not forecasts. However, as they depict the optimal, cost-minimizing outcome, results provide a guideline to policymakers and regulators regarding the desirable market outcome. A stochastic mixed-integer dispatch and investment model for the European natural gas infrastructure is developed as an optimization model taking the theoretical inter-dependencies into account. It is based on an extensive infrastructure database including long-distance transmission pipelines, LNG terminals and gas storage sites with a high level of spatial granularity. It is parameterized with assumptions on supply and demand developments as well as empirically derived infrastructure extension costs to perform model simulations of the European gas market until 2025. In the framework of the conservative demand forecast of the European Commission, efficient infrastructure expansion (starting from the 2010 infrastructure with all projects under construction being completed) is limited. The constant demand in combination with the newly created import capacities on the LNG (UK, Spain) and pipeline (Nord Stream, Medgaz) side means the gas infrastructure is well equipped to deal with declining European production. The reduction of flexibility provided by domestic production is compensated by flexible LNG imports if the global LNG market remains well supplied. Further scenario analyses illustrate the effects of changing the presumptions on supply and demand: A low LNG price does not increase LNG investments significantly, but reduces the requirements for pipeline investments in Europe, especially in East to West direction on the continent. An assumed decline in the flexibility of LNG imports in Europe, conversely, would greatly reduces efficient LNG capacity additions as the option to flexibly import natural gas is one of the favorable characteristics of such facilities. Consequently, investments in natural gas storage would have to increase substantially to provide flexibility through a different technology. This is also true if flexible LNG is replaced by either additional gas volumes imported via long-distance transmission pipelines from the Caspian region or if it is substituted by gas production from unconventional sources in Europe. Rising demand, intuitively, requires additional investments. The simulation of security of supply emergency scenarios demonstrates that redundant infrastructure capacities and gas stocks in excess of the volumes required to balance supply and demand can be efficient - even if the emergency probability is low. Modeling a one-month disruption of Russian transits via Ukraine and Belarus in 2020 shows that the infrastructure is rather resilient against such a threat. Reasons are alternative routes such as Nord Stream and the infrastructure investments made in the aftermath of the 2009 Ukraine transit disruption. Only limited additional investment in interconnection capacities between countries in Eastern Europe are found to be efficient. However, it also becomes evident that, with an emergency probability as low as two percent, it is efficient to stock up to 10 billion cubic meter of natural gas additionally in European gas storage facilities. Conversely, the infrastructure is found to be less resilient regarding a prolonged supply stop from North Africa as seen for Libya since February 2011. Italy would be affected particularly from a combined export disruption in Libya and Algeria, making significant investments in interconnections with Central and Northern Europe efficient. Additionally, further LNG import capacities would also be efficient to mitigate the consequences of a North African pipeline export stop. These investments in redundant import capacities become more efficient the higher the probability of the emergency. The analysis yields implications for natural gas infrastructure investments. With respect to the general results, it is illustrated how developments in one region (unconventional gas production, a new import corridor from the Caspian region) have significant implications for investments in geographically separated markets. The efficiency of investments in additional storage capacity is greatly affected by developments in the global LNG market and the composition of the European supply mix. Investments in redundant capacity to enhance security of supply are also found to be beneficial even if the probability of the respective emergency is low. However, it is also shown that a detailed analysis is required to identify specific, means-tested investment options - universal infrastructure standards may be of limited value. Furthermore, investments benefiting one region may efficiently take place outside the borders of that region. Important questions for further research, then, are (i) how these efficient investments can be incentivized through a regulatory framework and (ii) who bears their costs, which implicitly includes the question whether or not short-term security of supply is a public good. Regarding the applied analysis, further work may also target an improved modeling of interdependencies of the infrastructure system with natural gas consumption in the power and industry sectors (demand side management)

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.publishedVersio