Abstract

The rising share of intermittent renewable energy production in energy systems increasingly poses a threat to system stability and the price level in energy markets. However, the effects of renewable energy production onto electricity markets also give rise to new business opportunities. The expected increase in price differences increases the market potential for storage applications and combinations with renewable energy production. The value of storage depends critically on the operation of the storage system.In this study, we evaluate large-scale photovoltaic (PV) storage systems under uncertainty, as renewable energy production and electricity prices are fundamentally uncertain. In comparison to households who largely consume the stored energy themselves, the major business case for large-scale PV and storage systems is arbitrage trading on the electricity markets. The operation problem is formulated as a Markov decision process (MDP). Uncertainties of renewable energy production are integrated into an electricity price model using ARIMA-type approaches and regime switching. Due to non-stationarity and heteroskedasticity of the underlying processes, an appropriate stochastic modeling procedure is developed. The MDP is solved using stochastic dynamic programming (SDP) and recombining trees (RT) to reduce complexity taking into account the different time scales in which decisions have to be taken. We evaluate the solution of the SDP problem against Monte Carlo simulations with perfect foresight and against a storage dispatch heuristic. The program is applied to the German electricity and reserve power market to show the potential increase in storage value with higher price spreads, and evaluate a possible imposition of the feed-in levy onto energy directly stored from the common grid.

Highlights

  • Electricity systems and markets are increasingly challenged by uncertain production due to new technologies exploiting intermittent resources, such as wind or solar energy

  • As a goal of this study is to provide a comprehensive approach to the analysis of photovoltaic storage systems, we provide a short comparative evaluation study of the proposed stochastic dynamic programming (SDP) formulation

  • We show that our SDP approach performs better than algorithms that heuristically operate the storage intra-day

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Summary

Introduction

Electricity systems and markets are increasingly challenged by uncertain production due to new technologies exploiting intermittent resources, such as wind or solar energy. To the best knowledge of the authors, there is no study that examines the role of network charges for creating portfolio effects for PV storage systems These charges have to be paid by energy storage plants if they are dispatched at the spot market and if they are not considered as network operation components. The time-series models are applied to generate a large number of price and PV output series which are reduced to a recombining scenario tree as a basis for the SDP model that optimizes the dispatch of PV storage systems (Section 5) in the spot and reserve power market.

Literature review
Storage problem formulation as a Markov decision process
State of the system
Storage level update
Reserve power market restrictions
Contribution function/revenue
Objective function and Bellman equation
Modeling volatile electricity prices and PV power generation
Stochastic modeling of renewable energy feed-in
Stochastic modeling of electricity prices with merit-order effect
Solving the Markov decision problem using SDP
Scenario tree for strategies under uncertainty
Discretization of the general problem
Evaluation of a large-scale PV storage system
Validation of price and renewable energy production models
Evaluation and comparison of the SDP approach with other methodologies
Profitability of different storage capacities
Profitability of large-scale PV storage systems, and impact of network and RES charges
Critical reflection
Conclusions
Nomenclature

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