Abstract

The authors focus on a model for system operators that uses centralized scheduling of multiple flexibility assets and services to minimize the cost of managing problems with grid congestion, voltages, and losses. The model schedules flexibility assets using stochastic optimization for AC optimal power flow in an active distribution network. The novelty of the contribution lies in its focus on how the dynamic capabilities of the flexibility resources are defined with regard to how uncertainty is resolved in the model. The impact of uncertainty is studied by using well-known quality measures from stochastic programming, such as the value of the stochastic solution. Moreover, the authors introduce a new measure related to the impact of representing uncertainty and flexibility when considering reactive power. By changing the time attributes of flexibility assets, the authors show the impact of uncertainty and time structure on a scheduling problem. The uncertainties considered are price and load levels. The findings reveal that the quality of the scheduling of each flexibility resource depends on using a stochastic model with a rigorous consideration of time and uncertainty.

Highlights

  • The penetration of Distributed Energy Resources (DER), located close to where electricity is consumed, e.g., households or commercial buildings is increasing considerably in the last years

  • We investigated the impact of uncertainty in decisionmaking and the importance of how to represent the time dimension (i.e., duration and activation time) when scheduling flexibility assets and services as well as how uncertainty is resolved in optimal scheduling model

  • In this paper we have studied the scheduling of a portfolio of flexibility assets to solve voltage variation and grid congestion problems in an active distribution networks (ADNs)

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Summary

INTRODUCTION

The penetration of Distributed Energy Resources (DER), located close to where electricity is consumed, e.g., households or commercial buildings is increasing considerably in the last years. Authors in [23] used flexible demand and storage systems for an ADN with dynamic OPF modeling Their results showed the efficiency of the use of flexible demand and storage systems for ADNs. In a recent study, [24] present a method for two-stage hierarchical congestion management in ADNs with SOPs, tie switches, DERs, and a microgrid. Both sets of authors state the importance of uncertainty from the DER perspective They do not discuss the provision of reactive power from demand-side flexibility assets for grid operations according to the time dimension. The impact of uncertainty and time when scheduling each flexibility asset is examined by applying two variants of our optimal scheduling model Our evaluations use both this new quality measure and traditional ones such as the Value of the Stochastic Solution [30].

FLEXIBILITY ASSETS AND SERVICES
MATHEMATICAL MODEL
OBJECTIVE FUNCTION
CONGESTION CONSTRAINTS
IMPORT AND EXPORT CONSTRAINTS FROM AN EXTERNAL GRID
LOAD BALANCE CONSTRAINTS
LOAD SHIFTING CONSTRAINTS
STOCHASTICITY AND SCENARIO GENERATION
CASE STUDY FROM SOUTHERN NORWAY
STOCHASTIC RESULTS FOR WINTERTIME
THE IMPACT OF UNCERTAINTY AND TIME
CONCLUSION AND OUTLOOK
Full Text
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