Tomislav Capuder

Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty

Summary form only given. A key feature of smart grids is the use of demand side resources to provide flexibility to the energy system and thus increase its efficiency. Multienergy systems where different energy vectors such as gas, electricity, and heat are optimized simultaneously prove to be a valuable source of demand side flexibility. However, planning of such systems may be extremely challenging, particularly in the presence of long-term price uncertainty in the underlying energy vectors. In this light, this paper proposes a unified operation and planning optimization methodology for distributed multienergy generation (DMG) systems with the aim of assessing flexibility embedded in both operation and investment stages subject to long-term uncertainties. The proposed approach reflects real options thinking borrowed from finance, and is cast as a stochastic mixed integer linear program. The methodology is illustrated through a realistic U.K.-based DMG case study for district energy systems, with combined heat and power plant, electric heat pumps, and thermal energy storage. The results show that the proposed approach allows reduction in both expected cost and risk relative to other less flexible planning methods, thus potentially enhancing the business case of flexible DMG systems.

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems

Classical unit commitment (UC) algorithms may be extremely time-consuming when applied to large systems and for long-term simulations (for instance, a year) and may not consider all the features required for flexibility assessment, including analysis of different reserve types. In this light, this paper presents a novel flexibility-oriented unified formulation of a large-scale scheduling model considering multiple types of plants (including storage) and reserves, which can seamlessly model binary (BUC), mixed integer linear programming (MILP), and relaxed linear programming (LP) UC. Comparisons are carried out on several case studies for a reduced model of Great Britain, assessing loss of accuracy (as measured according to various metrics specifically introduced) against computational benefits in different renewables scenarios with more or less flexible systems. It is demonstrated how the computational time of the LP model is significantly less than the BUC and MILP approaches while capturing with relatively high precision all the relevant flexibility requirements and allocation of multiple types of reserves to different types of plants. The results indicate that the proposed fast LP model could be suitable for various computationally intensive flexibility studies (e.g., Monte Carlo simulations or planning), with significant reduction in simulation time and only minor errors relative to established MILP models.

Role and impact of coordinated EV charging on flexibility in low carbon power systems

The paper analyses the impact of Electric Vehicle (EV) integration into different power systems and their flexibility potential in mitigating the uncertainty and variability of renewable energy sources (RES) generation. The problem is cast as Mixed Integer Linear Programming (MILP) unit commitment, modelling different generation mix/technologies over a number of scenarios. The results, as expected, show that different EV charging strategies have different impacts on power system operation and unit scheduling. In addition, the analyses support the premises that the greater number of EVs, with coordinated charging strategies, can have environmental benefits in terms of reducing CO2 emissions in addition to reducing wind curtailment and system operation costs. These benefits are more obvious in low flexible power systems characterized by dominantly thermal power plants, while they are less pronounced in balanced hydro thermal systems.

Assessing the benefits of coordinated operation of aggregated distributed Multi-energy Generation

Evolving planning concepts of future low carbon energy districts take into account multiple energy resources and vectors but, despite the comprehensive approach, they often miss out on capturing interactions between those vectors and do not exploit all the opportunities available by such approach. This paper presents a mixed integer linear programming (MILP) algorithm for modeling operational behavior of aggregated district multi-energy generation units. The resulting aggregated generation portfolio can be seen as a city-level Multi-Energy Virtual Power Plant (MEVPP). Case study results clearly demonstrate benefits of different multi-energy configurations and their interactions under various market conditions, by using an “aggregation benefit matrix” specifically introduced. The outcomes provide strategic indications to energy companies, local authorities, policy makers, etc. in the direction of developing low carbon Smart Cities.

Modelling and assessment of the techno-economic and environmental performance of flexible Multi-Generation systems

This paper presents a unified techno-economic and environmental mathematical model for market driven operation optimization of different Distributed Multi-Generation (DMG) options in district heating schemes. The identified concepts of DMG are capable of providing significant operational benefits in that they have the flexibility to respond to electricity market signals, which is particularly important in the presence of a less flexible and more intermittency dominated power system. At the same time, the formulation allows assessment of the environmental benefits under different scenarios. The optimization formulation cast as a mixed integer linear program (MILP), is capable to incorporate the use of different multi-energy technologies and explicitly model inter-temporal constraints that allow assessment of the benefits of thermal storage, amongst the others.

Applications of clustering algorithms in long-term load forecasting

Load forecasting is one of the critical activities in electric power system planning. This paper presents clustering algorithms and their usage in load forecasting on a case study in Zagreb, Croatia. Load data acquisition is not always being systematically conducted in distribution networks and some data often has to be extrapolated. For such methods to work additional computation and grouping algorithms have to be used in addition to classical trend forecasting methods. Furthermore, the paper emphasizes on load forecasting in areas with no load history.

Analysis of incentives approach to renewable sources and cogeneration

The current feed-in tariff system for renewable energy sources and cogeneration in Croatia stimulates privileged producers exclusively for electricity delivered into the grid, both on transmission and distribution levels. Hence, the electricity consumed on the power plant site is not stimulated by the current feed-in tariff system. Therefore, most privileged producers, in order to receive incentives for entire electricity generation, have to deliver all of the generated electricity into the grid and separately buy the electricity from the electricity distribution company which requires significant supplementary investments. Furthermore, overall losses in electric grid are increased. This paper analyses the current feed-in tariff system and proposes an enhanced tariff system which allows metering of the generated energy. This, with on-site consumption metering or metering of electricity delivered to the distribution network, enables separate billing of electricity consumed at the plant site and delivered into the power grid. The paper analyzes the power flows and losses in the nearby electric grid in the current and proposed incentive system in three real-case scenarios.

A Comprehensive Approach for Maximizing Flexibility Benefits of Electric Vehicles

Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation.

Electricity markets overview — Market participation possibilities for renewable and distributed energy resources

The main idea of the paper is to provide a detailed analysis of market set-ups in Europe, North America and Australia as well as to define obstacles and potentials for full market integration for renewable and distributed energy resources. Comparing rules and market operational principles from different continents, elaborated through examples of most evolved markets, indicates how different technologies could benefit from offering market products on multiple time frame basis. In this line, recent European documents set a framework for non-discriminatory access to market for all entities. Since benefits for new entrants are maximized in case of co-optimized participation in multiple markets, the paper discusses how implementing segments and concepts from USA and Australian markets could help in achieving this in Europe.

Fast charging stations — Power and ancillary services provision

High penetration of variable renewable sources act as a heavy burden on conventional power system management and operation. Uncertainty in power systems expanded from demand side to generation side as well. Since new sources of imbalances have entered power system, it should be reorganized, automated and modernized. New providers of flexibility should be recognized and used in future power system planning and design. One of the possible technologies that can be used for flexibility provision are electric vehicles. Numerous fast charging stations are installed all over the world and such trend will continue in future. Depending on their operation, charging stations can act as flexibility providers but they can also further degrade systems flexibility if installed without any kind of energy buffer. This paper will present mixed integer linear model for flexibility studies of modern power systems with high penetration of variable renewable sources and electric vehicles. Results clearly show that smart planning of fast charging infrastructure can bring huge benefits to power system concerning costs, emissions, and variable renewable power curtailment.