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One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome computational intractability of these full optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, traditional TSA methods often apply a one-size-fits-all approach based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters – the resulting output error in optimization results – is not well addressed. In this paper, we plan to challenge this belief and show that output-error based TSA methods with theoretical underpinnings have unprecedented potential of computational efficiency and accuracy.

Under the current European market environment, transmission companies have to decide network expansion by maximizing social welfare. However, generation companies (GENCOs) decide their capacity expansion with the aim of maximizing their own profit. This process, in addition to the increasing penetration of renewable energy, storage and distributed generation, might represent a rupture between short and long-term signals. Therefore, this paper proposes a bi-level formulation for the generation and transmission coordination problem (GEPTEP). We consider a proactive framework in which a centralized TSO represents the upper level while the decentralized GENCOs, that trade in the market, represent the lower level. As a novel feature, an enhanced representative period framework is employed, which allows us to consider operation in both short and long-term storage technologies. A case study is presented to compare the results between perfect and imperfect competition in the market.

Some restructured power systems rely on audited cost information instead of competitive bids for the dispatch and pricing of electricity in real time, particularly in hydro systems in Latin America. Audited costs are also substituted for bids in U.S. markets when local market power is demonstrated to be present. Regulators that favor a cost-based design argue that this is more appropriate for systems with a small number of generation firms because it eliminates the possibilities for generators to behave strategically in the spot market, which is a main concern in bid-based markets. We discuss existing results on market power issues in cost- and bid-based designs and present a counterintuitive example, in which forcing spot prices to be equal to marginal costs in a concentrated market can actually yield lower social welfare than under a bid-based market design due to perverse investment incentives. Additionally, we discuss the difficulty of auditing the true opportunity costs of generators in cost-based markets and how this can lead to distorted dispatch schedules and prices, ultimately affecting the long-term economic efficiency of a system. An important example is opportunity costs that diverge from direct fuel costs due to energy or start limits, or other generator constraints. Most of these arise because of physical and financial inflexibilities that become more relevant with increasing shares of variable and unpredictable generation from renewables.

This paper introduces the open-source GridTool software. It is an easy-to-use tool to convert and simplify electricity grid data from the OpenStreetMap-project (OSM) for the use in power simulation models. The main advantages of the newly developed tool are its flexibility, customizability and its heuristic approach for identifying grid nodes. Instead of the lengthy and manual process of converting and implementing electricity grid data by hand, the GridTool shortens that task to seconds. During this process the OSM data gets reduced to essential elements needed in power simulation models. To our knowledge, there is no other open-source tool, that does this task out-of-the-box, is as easy-to-use, only requires a single program (MATLAB), and uses a heuristic approach.

This paper introduces the open-source Low-carbon Expansion Generation Optimization (LEGO) model. It is a multi-purpose tool to carry out numerous techno-economic analyses of the energy sector, ranging from short-term unit commitment to long-term generation and transmission expansion planning. Its highly flexible temporal structure permits both chronological and representative periods. LEGO is composed of thematic blocks that can be combined freely via data options: unit commitment constraints; DC- or AC-OPF formulations; battery degradation; rate of change of frequency inertia constraints; demand-side management; or Power-to-X in the form of the hydrogen sector. This unique feature allows to incorporate highly technical aspects into long-term investment analyses. To our knowledge there is no open-source model that offers this flexibility, which we hereby make freely available to the scientific community.

We propose a multi-objective bi-level optimization model for analyzing the different investment options (in natural gas pipelines and regasification terminals) within the EU framework under a market perspective, considering the different interest of market participants and the multiple criteria that need to be achieved simultaneously (i.e. market integration, security of supply and competition). The model consists of the objectives of the network planner at the upper level optimizing a multi-objective function and a lower level that represents the downstream European gas market. The model is used for the assessment of the optimal infrastructure investment in the North-South Gas Interconnections in Western Europe.