How Network Tariffs Impact the Optimal Design of Local Energy Systems: A Swiss Case Study

The integration of distributed energy resources (DERs) and novel loads like electric vehicles (EVs) or heat pumps (HPs) into local energy systems (LESs), which can function in harmony with the grid, is imperative for achieving a successful transition towards sustainable energy. However, it is vital to consider price signals that encompass system costs as well as Energy Strategy objectives when determining the optimal design and operation of LESs. In this paper, we are aiming to evaluate the potential impact of grid tariffs on LESs’ optimal solutions. We introduce an initial stage of our research endeavor by formulating a linear optimization algorithm that minimizes LESs energy costs. We conducted a case study in a Swiss municipality, wherein 85 LESs were subjected to different grid tariff schemes (including volumetric, power peak-based, and seasonal). Our algorithm was employed in order to identify the optimal LESs designs and evaluate the impact at the municipality level. Based on our findings, it can be inferred that the chosen grid tariff scheme heavily influences factors such as DER & Energy Storage Systems (ESSs) size and type selection, operation procedures of LESs, profitability rates and allocation of costs related to the power grid. We discuss the implications of these findings for network operators, LES owners and policymakers, and provide some recommendations for designing grid tariffs that can promote efficient and fair integration of DERs in LESs to achieve the Swiss Energy Strategy goals and ensure the reliability and cost efficiency of the energy system

Evaluate the impact of network tariffs on the Swiss energy transition. A fair cost distribution or a driver to reduce expensive network upgrades?

We aim to demonstrate the importance of an optimal network tariff model for future electric energy systems. Due to the lack of available household consumption data and the uncertainty of new actors impact (e.g. electric cars), there are no reliable environments to perform such simulations. We propose a new simulation environment in order to evaluate the impact of different network tariff models in a representative network region. The core of this new simulation environments are advanced machine learning models for household consumption, electric cars charger stations, solar panels productions and the local optimization of batteries behavior at the prosumer level. We propose a new method to evaluate the fairness of the network distribution costs. The presented results demonstrate that existing network tariffs will not be able to fairly distribute the costs and that the penetration of energy storage could put the system at risk if the network tariffs are not correctly adapted for this new environment

Asymptotic optimality of finite models for witsenhausen’s counterexample and beyond

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this chapter, we study the approximation of Witsenhausen’s counterexample and the Gaussian relay channel problem by using the results of the previous chapter. In particular, our goal is to establish that finite models obtained through the uniform quantization of the observation and action spaces result in a sequence of policies whose costs converge to the value function. We note that the operation of quantization has typically been the method to show that a non-linear policy can perform better than an optimal linear policy, both for Witsenhausen’s counterexample [10, 86] and the Gaussian relay channel problem [88, 152]. Our findings show that for a large class of problems, quantized policies not only may perform better than linear policies, but that they are actually almost optimal

Introduction and summary

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Control and optimization of dynamical systems in the presence of stochastic uncertainty is a mature field with a large range of applications. A comprehensive treatment of such problems can be found in excellent books and other resources including [7, 16, 29, 68, 84, 95, 104], and [6]. To date, there exist a nearly complete theory regarding the existence and structure of optimal solutions under various formulations as well as computational methods to obtain such optimal solutions for problems with finite state and control spaces. However, there still exist substantial computational challenges involving problems with large state and action spaces, such as standard Borel spaces. For such state and action spaces, obtaining optimal policies is in general computationally infeasible