Sergio Bruno, Massimo La Scala, Ugo Stecchi. Dipartimento di Elettrotecnica ed Elettronica

Transcription

1 Sergio Bruno, Massimo La Scala, Ugo Stecchi Dipartimento di Elettrotecnica ed Elettronica Politecnico di Bari Italy

2 Smart Grids at Politecnico di Bari This paper presents some preliminary results of an ongoing project on smart grids partially funded by Regione Puglia (3 years, about 1.3 M budget) It involves the Politecnico, two local energy distribution utilities (gas and electricity), and one SME AMGAS is the local distributor for natural gas in the city of Bari (around 350, inhabitants and 115,000 customers, 80,000 m 3 /h) AMET is the electricity DisCo for the city of Trani about AMET is the electricity DisCo for the city of Trani, about 65,000 inhabitants and 35,000 customers

3 Path towards a smart distribution grid In this path many distribution ib ti systems, including AMET, face a tough challenge traditionally passive networks built with a straightforward radial (or multi-radial) configuration minimal ability of monitoring and controlling power flows The distribution systems undergo profound modifications due to distributed energy resources (DERs), smart metering (the deployment of smart meters is nearly completed in Italy), storage/phevs It is crucial to exploit such chances derived by the natural development of the grid

4 In the next future More information, sensors, measurements, ICT, control resources and actuators will be available to Distribution Network Operators (DNOs) allowing to implement advanced monitoring and control functions SCADA/EMS (or DMS-Distribution Management System) is naturally fit for dealing with all these new elements Advanced DMS can be the core of a smart distribution grid, but most of monitoring and control tools must be readapted new operative and technical requirements and availability of new control resources must be taken into account

5 Proposed scheme for AMET ADMS CVR: Conservative Voltage Regulation SE: State Estimator DISTRIBUTION NETWORK ULTC VVO: Voltage Var Optimization SMS: Storage Management System ODPF: Optimal Distribution Power Flow RCS EDA: Environmental Data Acquisition ONR: Network Reconfiguration MMS: Maintanance Monitoring System AR: Adaptive Relaying CA: Contingencies Analysis SM: Switch Management FPFS: directional Fault and Power Failure System SDF: Supply and Demand Forecast TP: Topology Processor SC: Short Circuit analysis CVP: Capacitor/Voltage regulator Placement OTS: Operator Training Simulator DISTRIBUTION NETWORK ULTC: UnderLoad Tap Changer ADMS CVR SE VVO SMS ODPF EDA ONR MMS AR CA SM FPFS SDF TP Real time SCAD DA Signals from RTUs MDI SERVER DG SF AMI concentrator concentrator SCs DG AMR AMR AMR RCS: Remote Controlled Switches DG: Distributed Generation SCs: Switching Capacitors SF: Storage Facilities AMI MDI: Meter Data Integration AMR: Automatic Meter Reading SC Off line OTS CVP Environmental Monitoring Stations GIS INTERFACE CONTROL CENTER

6 Three Phase Optimal Power Flow Optimize i active and reactive control resources in the presence of unbalanced conditions (and in the extended real-time operating framework) Based on a Distribution Load Flow simulating software (OpenDSS by EPRI) Unbalanced conditions Object-oriented environment, improved representation of loads and other components Include new control variables and devices Load curtailment / active control Volt-Var Var Optimation (VVO) Conservative Voltage Regulation (CVR)

7 AMET distribution grid HV/MV representation ti only, 900 buses, 1000 lines, 500 load nodes, 100 switches (60 controllable) About 35 MW peak load demand Scarce penetration of DERs, but fast growth of photovoltaics (more than 60MW requested) Deployment of smart meters is 95% completed (AMI is a proprietary system) BUS A 20kV BUS B 20kV FEEDER 1 FEEDER 2 FEEDER 3 FEEDER 4 SUBTRANSMISSION NETWORK TR A 30 MVA FEEDER 5 FEEDER 6 FEEDER 7 FEEDER 8 SLACK 150kV TR B 25 MVA FEEDER 9 FEEDER 10 DISTRIBUTION SUBSTATION FEEDER 11 Feeder # Transfor mer I max [A] S max [kva] 1 TRA TRA TRA TRA TRB TRB TRB TRB TRB TRA TRA

8 Load control 35% load increase, congestion on TRA and feeders #3, #5 and #8 Penalty functions constraint power flows on transformers and currents on each feeder convergence after 12 iterations, about 3540 kw to be curtailed (about 8.4% of total active power) S iter 1 S 2 I 1 I 2 I 3 I 4 I 5 I 6 I 7 I 8 I 9 I 10 I 11 [kva] [kva] [A] [A] [A] [A] [A] [A] [A] [A] [A] [A] [A]

9 Conservative Voltage Regulation Minimization i i of active power exchanged at the substation ti Load models: 50% fixed impedance model, 25% constant active power and quadratic reactive, 25% linear active power and quadratic reactive Control variables: reactive power outputs and tap changers setpoints Active power decrease of about 8% C obj iter # C tot [p.u.] [p.u.] C V [p.u.] P 1 [kw] P 2 [kw]

10 Volt-Var Optimization Increase of distributed ib t d power injections can lead to counter-flows and be cause of overvoltages VVO aimed at constraining voltages in Control variables: reactive power outputs and tap changers setpoints 1,08 1,08 Before VVO minimum voltag ge magnitude [p.u u.] 1,06 1,04 1,02 1 After VVO.u.] maximum volta age magnitude [p. 1,06 1,04 1,02 1 Before VVO 0, ,98 After VVO Feeder # Feeder #

11 Conclusions Smart grids allow to embrace innovative monitoring i and control schemes Most of approaches can be derived by standard methodologies (OPF) that must be adapted for integrating new operative requirements and resources Computation is never an issue, especially in extended real-time domain, but time requirements of the communication system can be a relevant bottleneck The transition from simulation to implementation is the hardest step because of interoperability issues Smart meters are the most innovative contribution to smart distribution systems