An efficient algorithm for the provision of a day-ahead modulation service by a load aggregator

2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid

et al. 2013

Self Cite

This paper addresses the problem faced by a distribution system operator (DSO) when planning the operation of a network in the short-term. The problem is formulated in the context of high penetration of renewable energy sources (RES) and distributed generation (DG), and when flexible demand is available. The problem is expressed as a sequential decisionmaking problem under uncertainty, where, in the first stage, the DSO has to decide whether or not to reserve the availability of flexible demand, and, in the subsequent stages, can curtail the generation and modulate the available flexible loads. We analyze the relevance of this formulation on a small test system, discuss the assumptions made, compare our approach to related work, and indicate further research directions.

Section: Discussionmentioning

confidence: 99%

Active network management: Planning under uncertainty for exploiting load modulation

Gemine

Karangelos

2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid

et al. 2013

Self Cite

“…Taking as input the baselines provided by the BRPs and the characteristics of the network, the DSO provides an indicator of its flexibility needs in MW for each bus and each period in order to alleviate congestion in the lines. These needs are obtained by solving problem (6).…”

Section: Distribution System Operatormentioning

confidence: 99%

“…Distribution System Operators (DSO) need to have access to flexibility services in order to reduce their investment needs. The literature contains many papers showing the benefits of using such flexibility in distribution networks [1][2][3][4] and the methods to deliver this flexibility [5][6][7][8]. However, few studies have been performed on the way that the flexibility in distribution network can be exchanged as a commodity in an unbundled electric system.…”

Section: Introductionmentioning

confidence: 99%

DSIMA: A testbed for the quantitative analysis of interaction models within distribution networks

Mathieu

Louveaux

Sustainable Energy, Grids and Networks

et al. 2016

a b s t r a c tThis article proposes an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in which the distribution system operator, the transmission system operator, producers and retailers make their decisions based on mixed-integer linear programs. This testbed helps to highlight the characteristics of an interaction model, the benefits for the agents and eases the detection of unwanted or abusive behaviors which decreases the welfare. The testbed is implemented in Python and the optimization problems are encoded in the modeling language ZIMPL. A web interface is coupled with an instance generator using macroscopic parameters of the system such as the total power production. This testbed is, therefore, well suited to test the implemented interaction models on various scenarios and to extend the implementation to other models. Five interaction models developed with industrial partners are simulated over a year on a 75-bus test system. Simulations show that interaction models relying on active network management, as they have been developed, lead to substantial welfare even though they suffer from a lack of coordination between the DSO and the TSO. A conservative interaction model restricting grid users to an access range that is computed ahead of time to prevent any congestion, avoids shedding distributed generation but considerably restrains the amount of distributed production.

“…These loads are typically electrical boilers and heaters. The model proposed in this paper is a tank model similar to the one proposed in [11]. Let us consider a flexible load k. This load can only consume power during off-peak periods.…”

Section: Load Modelingmentioning

confidence: 99%

Optimal assignment of off-peak hours to lower curtailments in the distribution network

Merciadri

Mathieu

IEEE PES Innovative Smart Grid Technologies, Europe

et al. 2014

Self Cite

Abstract-We consider a price signal with two settings: offpeak tariff and on-peak tariff. Some loads are connected to specific electricity meters which allow the consumption of power only in off-peak periods. Historically, off-peak periods were located during the night and on-peak periods during the day. Changing the assignment of off-peak periods is an easy method for distribution system operators to access to the flexibility of small consumers. This solution can be implemented quickly as the infrastructure needed already exists in some countries.We propose a mixed-integer linear model to assign optimally the off-peak hours so as to minimize a societal cost. This cost gathers together the cost of electricity, the financial losses due to energy curtailments of photovoltaic installations and the loads' wellbeing. Our model considers automatic tripping of inverters and constraints of the electrical distribution networks. Simulation results show that the new disposition of off-peak hours could reduce significantly the photovoltaic energy curtailed in the summer.Index Terms-Mathematical programming, Optimization, Smart grids, Sustainable development. NOMENCLATUREThis section defines the main symbols used in this paper.T \ {0} S nodes S h nodes to which houses are connected K flexible loads G distinct groups g(k) group g ∈ G to which load k belongs K(i) flexible loads connected to node i D days of the time horizon Parameters'pr' is a period. If the chosen timestep is 1 h, pr = h.C ij,t capacity of link (i, j) in period t α t price for 1 kW pr in period t π c consumer's money gain having produced 1 kW pr λ cost for a loss of 1 kW pr on the consumer side η i,t maximum power injected at node i in period t ρ i,t curtailable power production at node i in period t ζ i,t static load power consumption at node i in period t β k,t amount of off-peak periods added tonumber of off-peak periods for group g during day d µ daily maximum number of rate switchings Variables l ij,t power going from node i to node j in period t n i,t power balance at node i in period t r i,t curtailed quantity at node i in period t s k,t state of load k at the end of period t ≥ 0 x k,t fraction of period during which load k consumes power in period t u g,t equals 1 if consumer group g is off-peak in period t, 0 otherwise w k,t auxiliary variable needed in order to define x k,t p i,t variable power consumed at node i in period t z g,t equals 1 if there is a rate switching between periods t − 1 and t