The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a  progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models. 

The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models.

The development of power system models relies on large data sets, the quality, and openness of which determine the models' accuracy and efficacy. There are numerous data sources publicly available, for the Nordic region though spread across diverse repositories and organised in different schemas. Their combination and cross-validation is a tedious process, usually undertaken by researchers or practitioners, in an ad hoc fashion. This paper identifies, compares, and validates various publicly available power systems data sets, pertaining to the Nordic region. Their quality, level of detail, granularity, and similar characteristics are highlighted, while the challenges of data warehousing procedures are addressed, for the purpose to be used in realistic power systems-related case studies.

The surge in intermittent renewable energy sources has led to larger volumes traded in the short-term electricity markets. As a result, the problem of optimally trading in the cross-border continuous intraday (CID) electricity market has come under the limelight. In this paper, we propose a multistage stochastic programming model to determine the participation of a virtual power plant (VPP), comprising wind power, hydropower, and thermal power portfolio, in a cross-border CID market. The VPP determines the order volumes to be submitted at the market price by assessing the available orders in other bidding areas. We also present an order clearing model that accounts for the cross-border transmission capacities along with the liquidity in the CID market. The risk-averse behavior of the VPP is captured by the nested conditional value at risk (CVaR) formulation. In order to demonstrate the functionality of the proposed model, several case studies are performed by constructing data models based on historical trading data obtained from the Nord Pool.

The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intradaymarkets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.

Open multi-energy system models are becoming increasingly more important for transitioning into a sustainable energy system. Open-source energy modelling tools like SpineOpt can provide such a transparent and flexible framework for performing complex and realistic case studies. In this paper, a short-term electricity and heat dispatch model with different types of Combined Heat and Power units and Power-to-Heat systems is implemented and simulated, using SpineOpt. The model’s performance and efficacy is evaluated against the commercial equation-based optimisation library GUROBIPY with good results. The model and the data are made openly available.

The tremendous complexity of modern distribution systems calls for alternative coordination architectures, supported by smart, self-adaptable, and, to a large degree, environment-agnostic algorithms. In this article, we discuss decentralized and distributed coordination architectures for the operation of active distribution grids aiming at effectively coping with their complexity. We present relevant methods and algorithms under the framework of multiagent systems (MASs) and decentralized decision-making associated with handling different parts of the optimal grid operation. The decision-making models are based on distributed optimization algorithms using consensus/gossip models, bioinspired algorithms from the field of population dynamics, and a method for decomposing the power-flow model. The developed techniques aim at matching production with demand in microgrids, settling the short-term energy imbalances at the distribution grid level, mitigating voltage deviations, and resolving distribution grid congestions in real-time operation. The algorithms are implemented as MAS-based software platforms, able to aggregate diverse DG units and flexible loads. Results are provided from the theoretical simulation-based models and demonstrations of the operational techniques in actual pilot sites. The applied implementations have been performed in a smart grid pilot site, for which MAS platforms have been developed and tested. 

The Spine Toolbox is open-source software for defining, managing, simulating and optimising energy system models. It gives the user the ability to collect, create, organise, and validate model input data, execute a model with selected data and finally archive and visualise results/output data. Spine Toolbox has been designed and developed to support the creation and execution of multivector energy integration models. It conveniently facilitates the linking of models with different scopes, or spatio-temporal resolutions, through the user interface. The models can be organised as a direct acyclic graph and efficiently executed through the embedded workflow management engine. The software helps users to import and manage data, define models and scenarios and orchestrate projects. It supports a self-contained and shareable entity-relationship data structure for storing model parameter values and the associated data. The software is developed using the latest Python environment and supports the execution of plugins. It is shipped in an installation package as a desktop application for different operating systems.

The transition towards more sustainable energy systems poses new requirements on energy system models. New challenges include representing more uncertainties, including short-term detail in long-term planning models, allowing for more integration across energy sectors, and dealing with increased model complexities. SpineOpt is a flexible, open-source, energy system modelling framework for performing operational and planning studies, consisting of a wide spectrum of novel tools and functionalities. The most salient features of SpineOpt include a generic data structure, flexible temporal and spatial structures, a comprehensive representation of uncertainties, and model decomposition capabilities to reduce the computational complexity. These enable the implementation of highly diverse case studies. SpineOpt's features are presented through several publicly -available applications. An illustrative case study presents the impact of different temporal resolutions and stochastic structures in a co-optimised electricity and gas network. Using a lower temporal resolution in different parts of the model leads to a lower computational time (44%-98% reductions), while the total system cost varies only slightly (-1.22-1.39%). This implies that modellers experiencing computational issues should choose a high level of temporal accuracy only when needed.

Small-scale hydropower production is a comparatively small power resource, but it is valuable as it is CO2-free, renewable, and flexible.~However, small-scale hydropower owners in Sweden often operate their plants empirically and without coordinating with other plant owners of the same river system, possibly resulting in suboptimal water usage.~This paper investigates two different hydropower planning strategies for small-scale hydro in a realistic case study from historical hydrological data. Two scheduling models are formulated and compared; a coordinated scheduling model assumes centralised information sharing to calculate optimal planning results, and a successive, greedy planning process, where each power plant determines its operational schedule individually and passively, depending on the water discharged from the upstream plants.