Florian Kienzle

Valuing Investments in Multi-Energy Conversion, Storage, and Demand-Side Management Systems Under Uncertainty

In this paper, a financial valuation method for energy hubs with conversion, storage, and demand-side management (DSM) capabilities is proposed. An energy hub is an integrated system of units, e.g., a combined heat and power plant and a heat storage, which allows the conversion and storage of multiple energy carriers. In this paper, an extended energy hub model is presented which additionally takes into account the possibility of performing DSM with the load(s) connected to the hub output. Taking into account the energy hubs flexibility to change its output power(s), its economic value is determined with a method based on Monte Carlo simulation. This method calculates an optimal dispatch of the hub for a large amount of possible price paths of the input and output energy carriers. By means of the proposed energy hub Monte Carlo valuation model, integrated systems of multi-energy conversion and storage devices can be valued together with load management schemes. In doing so, the energy hubs ability to flexibly adapt its output to uncertain and volatile market prices is explicitly considered.

Modeling interconnected national energy systems using an energy hub approach

This paper describes an approach to model interconnected national energy systems using the concept of energy hubs. Each country is modeled as an energy hub, characterized by the national generation infrastructures for heat and electricity, the demand for heat and electricity as well as properties detailing mobility demand. Countries are interconnected via electricity and gas networks, i.e. it is possible to import or export electricity and/or gas. The paper gives a short introduction to the concept of energy hubs and describes the extensions of the concept to account for national multi-carrier energy systems. In a subsequent part the network model is introduced. The different constituents (generation, demand, transmission infrastructures) form an optimization problem, where a numerical solution approach is combined with particle swarm optimization. Furthermore, it is described how the relevant data concerning demand, generation and network infrastructures was obtained. The paper is concluded with three case studies demonstrating the applicability of the proposed approach.

Multi-energy delivery infrastructures for the future

This paper presents an overview of the methods and modeling concepts developed in the framework of the project ¿Vision of Future Energy Networks¿. It outlines the fundamentals of the project comprising (a) the development of modeling and analysis tools for systems involving multiple energy carriers (e.g. electricity, heat and gas) and (b) a Greenfield approach for the integrated planning and realization of future energy infrastructures. This paper especially focusses on a layout procedure for the so-called Energy Interconnector being a device for combined transportation of electrical, chemical and thermal energy. Concluding the paper, the potential application of the Energy Interconnector is illustrated with an exemplary case study. The presented modeling and analysis framework can be used to evaluate multi-energy networks as development option for future energy networks.

Maximizing exergy efficiency in multi-carrier energy systems

In this paper a model for maximizing exergy efficiency in multi-carrier energy systems is introduced. Based on modeling concepts developed in the project “Vision of Future Energy Network”, e.g. the Energy Hub concept, exergy is modeled in the context of energy systems that involve multiple energy carriers such as electricity, natural gas and heat. In the context of this integrated consideration of multiple energy carriers, the exergy approach allows to take into account the quality of different energy carriers. Hence this modeling approach provides the possibility to identify from a system perspective how the available energy content of different energy carriers can be exploited as efficiently as possible to satisfy a given demand for final energy carriers. In order to illustrate the proposed exergy analysis method, we compare it with a previously developed cost optimization and apply both methods to an example system consisting of an electricity and natural gas system interconnected by Energy Hubs.

Location-dependent valuation of energy hubs with storage in multi-carrier energy systems

In this paper a valuation method for energy hubs containing storage devices is presented. An energy hub is an integrated system of units, e.g. a combined heat and power (CHP) plant and a battery, which allows the conversion and storage of multiple energy carriers. To determine the economic value of an energy hub, its operation is modeled as a series of call options. Taking into account the hubs flexibility to change its output power(s), this series of call options is valued with a Monte Carlo simulation method that calculates an optimal dispatch of the hub for a large amount of possible price paths of the input and output energy carriers. Using the nodal prices from an optimal power flow analysis (OPF) of a system of interconnected energy hubs, each hub can be valued depending on its location. By means of the proposed energy hub real options model, integrated systems of conversion and storage devices can be valued considering both their position in the network and their ability to flexibly adapt their operation to volatile market prices.

A greenfield approach to the future supply of multiple energy carriers

In this paper a model for the design of future multi-energy systems including electricity is presented. In this context a greenfield approach is applied, i.e. in a first step efficient portfolios for the supply of multiple energy carriers (e.g. electrical energy, heat, and chemical energy carriers) are calculated for a certain target year. In a second step, an optimal transition path from a portfolio given today to the desired optimal portfolio in the future is determined. The method presented in this paper is based on a single-period mean-variance portfolio model, which is adapted to be used for portfolios providing multiple energy carriers. Optimal transition paths are calculated applying a dynamic programming method that maximizes utility along the transition path. The proposed method is illustrated applying it to a combined heat and power portfolio consisting of a set of small-scale generation technologies.

Comprehensive performance and incertitude analysis of multi-energy portfolios

In this paper a model for the assessment of efficient energy generation portfolios is presented. The total energy output can consist of multiple energy carriers such as electricity, heat or chemical energy carriers. In order to take into account multiple aspects of performance and different types of incertitude influencing the long-term energy planning process, two different methods are combined. Mean-variance portfolio theory and multi-criteria diversity analysis are applied resulting in a multi-objective optimization problem. Solving this optimization problem for multi-energy portfolios allows for identifying energy generation technologies mixes that both minimize the exposure to various kinds of incertitude and maximize performance with respect to multiple performance criteria. In this way multi-energy portfolios are analyzed in a comprehensive and systematic way.