The 2030 and 2050 EU’s carbon reduction targets call for significant changes in our energy system: more flexibility, more active involvement of all stakeholders and more collaboration to enable least-cost integration of higher deployment of variable renewable energy sources. Operating the electricity system with the highly targeted shares of RES will only be possible and affordable if both the grid and the generation assets evolve towards a system designed to maximise its capacity to host such amounts of RES. This requires optimising existing assets and new investments, making the best use of all flexibilities (considering the geographical location and services they provide to the system) and developing new services to support the energy system. We believe that an integrated representation of the system is necessary to achieve European climate objectives cost-effectively for all the stakeholders participating in the system operation and development.

However, such an integrated representation will require overcoming significant technical hurdles in order to allow a set of different but highly interconnected models (strategic investment – operational simulation – multimodal system integration) to work in synergy while retaining the modularity (possibility of representing only sub-parts of the system, either functionally, geographically or on a specific time horizon, with a specific time resolution, or replacing every model and algorithm by another one). This is necessary for tailoring the tool to the different needs of various stakeholders.

In order to address those challenges, the plan4res project has delivered :

  • An end-to-end planning and operation tool, composed of a set of optimisation models based on integrated modelling of the European Energy System, including :
    • Dynamics of energy system development and transformation, in particular with and without perfect foresight;
    • A representation of the interactions of multimodal energy vectors and the impact on available flexibility;
    • A subnational representation of the grid and potential cross-border energy exchange;
    • A realistic dispatch with a precise technical description of all generation assets (including hydro storage);
    • A representation of the new challenges of the grid facing large shares of RES, e.g. frequency stability;
    • A proper representation of flexibility needs and flexibility potential provided by all assets, including multi-energy services.
  • An IT platform for providing seamless access to data and high-performance computing resources, catering for flexible models (easily replacing submodels and the corresponding efficient solution algorithms) and workflows;
  • Efficient solving algorithms;
  • A database of public data used for modelling;
  • 3 Case studies showing the tool’s functionalities and relevance regarding the uses as mentioned above, especially key advances included in plan4res: multi-energy integration, investment planning under uncertainties, flexibility cost integration within a pan-European approach.

In this context, we would like to ask your view on how such tools, IT platform, solving algorithms, and public data could be used to address the challenges you experience or think will emerge in the near future triggered by the increased penetration of RES.

The survey is divided into few sets of questions to collect information regarding:

  • Your background
  • Your view on
    • Tool 1: Multimodal energy system modelling
    • Tool 2: Transmission planning under uncertainty
    • Tool 3: Seasonal Storage valuation and Unit commitment
    • IT platform
    • Solution algorithms

Each survey will take 1-2 mins to complete.

In each section below, the overview of the tools and the case studies is described first before we ask you to complete the survey using the link provided as shown below.



First, please provide your background [click this link]


Tool 1: Multimodal energy system modelling

Description of the tool

This modelling toolset focuses on incorporating different energy sectors and sector coupling to reduce the overall CO2 emissions of the energy system and increase the flexibility of the energy system. The key aspect of the complimentary case study is a projection of the multimodal multi-regional energy mix plus the investment trajectory along the entire pathway, providing a detailed view on the overall energy generation and demand, including sectors beyond electricity, such as gas and fuel, thermal heat, cold and mobility and their interactions.

Usually, multimodal energy modelling and analysis is performed in two consecutive steps.

  • Step 1 optimises the entire investment pathway using an aggregated view, high-level operational schedules and optimal energy mix and investments for each interval along the pathway.

Results from step 1 represent a macroeconomic view of the energy system. The use of a relatively low resolution allows to include a vast number of competing technologies – already historically installed ones and potential new ones – from all addressed sectors, i.e. electricity, gas and fuel, thermal heat and cold, and mobility. Moreover, it allows to do an optimisation in which the solver can freely choose the best fitting from all competing technologies from any sector to achieve the stated targets, usually minimising total expenditures (TOTEX) and as side constraint meet given CO2 emission restrictions. It allows considering the whole pathway of the energy transition by solving one large linear problem at once. This methodology is advantageous for analysing the impact of sector coupling.

The focus of step 1 is on determining the installed capacity of the optimised energy mix, including optimal investments along the pathway and considering technology fleets and not individual plant operation. This output serves as input to the second step.

  • Step 2 refines these results for a specific focus years along the pathway, focusing on electricity generation and consumption, and on energy transport (incl. electricity and gas).

To get a more detailed view of the energy system, a detailed bottom-up approach is used to distribute energy mix and installed capacities to higher spatial resolution and optimise power plant operation on higher regional resolution considering technical constraints. Using the operation schedules of central and decentral generation units, electricity grid analysis are performed using power flow and congestion management tools.

Additionally, regions are coupled not only by the electric transmission grid but also those constraints from the existing gas transport network, which have to be considered. Therefore, the results of Step 1 are further evaluated using a stationary gas network optimisation model based on physical flows. Flexibility potentials of power-to-gas in a coupled energy system and its constraints are assessed by not only modelling the electric grid transport side but also using a gas grid model for validating location and operation schedules from gas consumers (power plants, heating) and generators (imports, production and power-to-gas), too.

Tool functionality:

The case study is studied in two consecutive steps (compare Figure 1).

Step 1 focuses on the optimal investment pathway using an aggregated view and providing the optimal energy mix per year and demand or generation KPIs per technology (treated as aggregated technology fleets).

Step 2 refines these results for specific focus years by using a detailed bottom-up approach, including a breakdown of installed capacities to higher spatial resolution and consideration of technical constraints (e.g. minimum up-/downtimes) for power plants.

Additionally, flexibility potentials of Power2Gas are considered in the electricity system and validated using a gas flow grid model.

Case studies:

Case study 1 focuses on finding a multimodal European energy concept for achieving COP 21 goals with perfect foresight, considering sector coupling of electricity, heat and cold, mobility and fuel/gas. It also focuses on sector coupling as a core element, i.e., incorporating different energy sectors to reduce the overall CO2 emissions of the energy system and increase the energy system’s flexibility.

Below is the list of questions addressed by Tool 1:

  • What is the optimal transformation pathway for the European energy system to meet the COP21 targets in 2050?
  • What will the optimal future energy mix look like?
  • How can we reach the COP21 goal with a cost-effective investment pathway?
  • What impact has sector coupling on the future generation fleet, e.g. the potential role of emerging technologies like power-to-heat, eMobility and power-to-gas?

Further objectives are to assess the tool’s adequacy and relevance to analyse

  • The investment trajectory for an integrated energy system for a set of countries
  • The impact of extended pan-European cross-border energy exchange
  • The impact of sector coupling on the future multimodal energy mix
  • The impact of promising emerging technologies on the integrated energy system




Please provide your view on Tool 1 [click this link]


Tool 2: Transmission planning under uncertainty


The transmission planning model under uncertainty is formulated as Mixed Integer Linear Programming (MILP) problem where long-term uncertainty is modelled by a stochastic process described by a multistage scenario tree. The objective function involves minimising the expected system cost across the scenario tree subject to investment and operation constraints at many operating points.

The tool allows considering non-sequential investment affected by building delays along multiple epochs.  The formulation can identify valuable strategic opportunities which postpone decisions until more information is revealed.

The tool can accommodate multiple line reinforcements and energy storage technology options.  Each line reinforcement option is characterised by fixed and capacity investment cost. Exclusive investment options are considered.

Different operating conditions are captured at the operation levels, defining representative temporal blocks of appropriate length and temporal resolutions. The tool includes the operation of conventional power generators, wind and PV generation, storage assets, Pump-Hydro Storage, Hydro run of river and Hydro-reservoir.

It accepts the definition of general scenario tree structures to model uncertainty in wind farm deployment patterns and future demand growth and storage cost investment.

The inputs of the planning tools are

  • Topology of the network
  • Assets in the network
  • Investment and operation cost of the assets and their technological characteristics
  • Scenario tree structure and transition probabilities
  • Temporal blocks and the temporal resolution inside each block
  • Number of epochs

The outputs

  • Optimal cost or its guaranteed lower and upper bound
  • Investment decisions/strategies
  • Optimal operation in the defined temporal blocks

Tool functionality:

The tool can be used to answer multiple questions

  • Strategic planning under a different number of uncertainties
  • Deterministic studies for a single scenario
  • Sensitivity analysis
  • Select type, capacity and location of storage assets
  • Select size, type and location of line reinforcements
  • Analyse the effect of different temporal scales in the epochs and the temporal blocks

Case studies:

Case Study 2 involves network planning under uncertainty, with the scope is the European energy system at the transmission system level, with investments being focused on energy storage and interconnectors – which form the conventional reinforcement decisions. The planning horizon spans 2050-2060 and is split into several stages/epochs, representing the decision points, I.e. the points in time where the network planner will decide when/ how much / which type of investments to make. This analysis will identify the effect of uncertainty on the investment decision-making process and identify the major trends.

The study outcomes involved identifying the optimal transmission investment strategy for a given scenario tree (i.e. structured description of long-term uncertainty and its potential resolution structure over time). Note that the optimal strategy is not simply an investment schedule but a comprehensive policy of decisions that consider optimal recourse actions in the case of unfavourable/favourable developments. Moreover, it is the quantification of the option value of smart technologies. This will aid practitioners to concretely answer questions on the value of investing in a flow control asset as an interim measure until the future generation locations are known with more certainty?

The application of this tool lies in the provision of support to transmission network planners and policymakers to quantify the benefit of different network reinforcements, smart technologies, and early-deployment innovation schemes.

Possible application of the tool can be in the transmission system planning, and distribution system planning of different regions such as the North Sea and the Southern parts of Europe characterised by solar PV penetration under uncertain deployment patterns.

The tool also is characterised by its flexibility in terms of objectives as different objectives can be applied, such as finding the minimax regret and the expected system minimisation objectives.


Please provide your view on Tool 2 [click this link]


Tool 3: Seasonal Storage valuation and Unit commitment


The main objective of this tool is to simulate and evaluate the feasibility and costs of a given long-term electricity system scenario (e.g. an output of Tools 1 and/or 2). This tool is composed of 2 embedded layers:

  • The Seasonal Storage Valuation Model (SSV) solves a mid-term (usually annual) problem, which consists of evaluating the expected operation cost and computing strategies for mid-term storages for a given electricity mix (generation, transmission, storage), while dealing with uncertainties related to demand, inflows, renewable generation potentials, and power plant shortages. The problem is formulated as a stochastic optimisation problem and solved using an SDDP (Stochastic Dual Dynamic Problem) algorithm, in which the evaluation of each sub-problem is done by the ‘EUC’ below.
  • The European unit commitment problem (EUC) solves the short-term horizon problem (usually daily or weekly), thus computing schedules for all assets while taking into account the “value” that seasonal storage units can bring to the system. Various kinds of flexibilities involving generation, storage and consumption are included, for example, the dynamic operation of power plants, storages, and curtailment or shifting of consumers loads. The problem is formulated as a deterministic mixed integers linear (or quadratic) problem and is solved using a Lagrangian relaxation algorithm.

The inputs of the SSV/EUC tools are

  • Topology of the (simplified) electricity network
  • Generation and storage assets in the network
  • Operation cost and technological characteristics of assets
  • Time series of uncertain variables: electricity demand, inflows, generation capacity profiles of wind/PV/run of river power, availability of thermal power plants

The outputs

  • Management strategies for long/mid-term storages (Bellman values)
  • Scenarised hourly operation schedules of assets
  • Operation costs

Tool functionality:

The tool can be used to answer multiple questions

  • Evaluate the feasibility and cost of a given electricity mix
  • Compute operations schedules
  • Analyse the value of different kinds of flexibilities (e.g. demand response…)
  • Sensitivity analysis

Case studies:

Case Study 3 is focused on the pan-European electricity system, including flexibilities provided to the electricity grid by sector-coupling assets. It aims to assess the overall system costs within the context of massive RES integration. Special emphasis is placed on costs that are usually not taken into account in energy models (e.g. costs to maintain frequency stability, costs related to the operation of flexible assets, etc.). In summary, this case study aims to evaluate the costs of RES integration and the value of flexibility.

The case study considers major sources of short-term and long-term uncertainty affecting the energy system, including the electricity demand uncertainty caused by meteorological uncertainties and long-term trends such as the electrification of transport, uncertainty related to renewable sources of generation, water inflows in hydro-reservoirs and unexpected unavailability of assets.  The flexibility associated with generation, storage and consumption is modelled in detail. For instance, a range of constraints is included in the model such as the dynamic operation constraints of power plants (ramping constraints and minimum shut-down duration), dynamic operation of storage (including battery and hydro systems), as well as Demand-Response at various levels (e.g. household load-shifting or load curtailment). Finally, the electricity transmission grid is represented by a ‘cluster’, where a country may be decomposed into a limited number of ‘nodes’ connected with lines of limited transmission capacity

EUC/SSV will be used to assess:

  • The impact of different levels of RES integration on the European system costs broken down by categories: total, generation (investment and operational), transmission and distribution;
  • The value of flexibility, i.e., the system cost reduction coming from using the flexibility potentials of the different system assets, which will be computed by simulating different configurations: non-flexible or flexible volatile RES (depending on whether they can be curtailed or not, or contributed to ancillary services or not), activation or not of flexibilities from different kinds of storages or demand-response, etc.




Please provide your view on Tool 3 [click this link]


IT platform


We found that the usage of IT varies widely among practitioners, including manual, (sometimes elaborate) spreadsheet calculations with or without automatic database access, specialised “small” custom programs running on commodity hardware, (free or commercial) mathematical programming models, parameterised by hand or in an automatic workflow, to large-scale automated scenario computations on high-performance computing systems.

To better judge the possible deployment hurdles for the plan4res tool, we would like to understand better your available, desired, and feasible IT infrastructure and the constraints put on its usage.




Please provide your view on IT platform [click this link]


Solution algorithms


The plan4res project has developed solution algorithms for huge-scale, highly structured optimisation problems with the nested, heterogeneous structure. These are based on an innovative modelling system, SMS++, whose role is to facilitate the modular development of models with a high hierarchical structure. The system makes it possible to easily define and implement nested decomposition methods, allowing specialised solvers of different types to be used at all levels of the approach where the underlying structure permits it. This flexibility, as well as the built-in support for parallel computation, allows tackling problems whose size and complexity is way beyond that of monolithic general-purpose solvers while retaining the possibility of using the latter (and in particular, the open-source SCIP solver developed by ZIB) at all stages where they are effective.

A possible pain point of SMS++ is the current lack of a direct interface with established commercial and open-source algebraic modelling systems (AMPL, Gams, COLIOP, ZIMPL, JuMP, …), although support and/or extension of these is foreseen in the future. While the system has a number of features dedicated to making it easier to develop models, among which the built-in modularity due to the concept of block, currently the use of SMS++ requires direct development in the C++ language, which may pose a significant barrier in some environments.

To further improve the development of SMS++, we would like to ask your experiences on optimisation.




Please provide your view on Solution algorithms [click this link]


We thank you for your time spent taking this survey. The outcome of this survey will be analysed to inform further development of the plan4res tools.