Using Optimal Power Flow for Management of Power Flows in Active Distribution Networks within Thermal constraints M. J. Dolan University of Strathclyde
[email protected]
E. M. Davidson University of Strathclyde
[email protected]
G. W. Ault University of Strathclyde
[email protected]
F. Coffele University of Strathclyde
[email protected]
I. Kockar University of Strathclyde
[email protected]
J. R. McDonald University of Strathclyde
[email protected]
The Autonomous Regional Active Network Management System (AuRA-NMS) research consortium is investigating real time control algorithms for voltage control, power flow management (PFM) and restoration. The functionality offered by this developmental distributed control architecture meets the requirements of being a robust 'plug and play' platform that must be safe, secure and have the ability to be flexible and extensible. Ongoing work has been investigating and comparing novel and existing control techniques for realtime power flow management which includes a current tracing method, a constraint programming approach and an optimal power flow (OPF) technique. This paper will discuss the properties of OPF and how these properties can be adapted, augmented and applied in a real time environment for solving distribution network power flow constraint problems using, in essence, real-time information available for normally nondispatchable renewable generation. Proposed OPF problem formulations and case studies will be used to show how research undertaken to date will be taken forward to development and demonstration of the OPF algorithms for power flow constraint management. Abstract-
Index Terms-- Active Network Management, Distributed Generation, Optimal Power Flow, Power Flow Management
I.
INTRODUCTION
The numbers of distributed generation (DG) planning and consent applications have shown significant increases in the last decade in response to European and domestic policies for carbon reduction. The increasing DG connections to the UK distribution networks are due to the close proximity of those networks to the natural resources being exploited (e.g. wind and marine). The intermittent nature of renewable energy generation, in particular wind generators, makes predicting network power flow magnitudes and directions difficult. Planning engineers address this uncertainty through understandably conservative planning guidelines to ensure network constraints are not breached and to determine whether network reinforcements are required. This approach can leave some headroom for additional DG access if monitoring and control is introduced to the currently passive networks [1] [2]. A method of avoiding the high capital
expenditure on network asset upgrades has been identified as Active Network Management (ANM) [3][4] which is based around monitoring and control as noted. Seven universities, two distribution network operators and ABB form the Autonomous Regional Active Network Management System (AuRA-NMS) [5][6] research consortium that is investigating real time control algorithms for voltage control [7], power flow management (PFM) [8] and restoration. Ongoing research is examining the characteristics of three possible PFM methods: • • •
Current Tracing the Constraint Satisfaction Problem (CSP) OPF
In particular, exploration of their ability to detect and offer appropriate solutions to network thermal constraints in a suitable timescale [9]. The inclusion of DG network access contractual agreements is also being investigated and shall be discussed. The objectives of the PFM solution search algorithms are to ensure that if a network thermal constraint is breached then the available control actions that can be taken for alleviation of the overload are executed. These control actions vary from network to network. However, AuRANMS recognises the different control actions and offers a solution that is plausible for the given networks and the location of any thermal breach. The properties of OPF are explored in this paper to identify the prospect of OPF being used in an autonomous control environment. Exhibited within the paper are the initial results from two case study networks and further discussions as to how the research can be taken forward. II.
POWER
FLow MANAGEMENT
Power flows within current distribution networks, with increasing distributed generation, can be unpredictable and variable due to the numerous power injection points. The nature in which most renewable generators output energy, the
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daily load variations, the limited measuring points and the current state estimation techniques widen the uncertainty of when an overload might occur and when it would be detected. It is therefore a function of AuRA-NMS to automatically detect and offer solutions to thermal overloads. There are a number of candidate techniques that can be formulated for responsive management of network power flows and that could be used within AURA-NMS. However, the chosen approach must fulfil the flexible and extensible desire of the AuRA-NMS plug and play architecture incorporating network commercial connection agreements.
III. OPTIMAL POWER FLOW
OPF has been investigated and refined for use in power system applications over several years (since circa 1960s ) and used as both an on-line and off-line analytical tool [1O][1l][12] . The OPF generation dispatch algorithm is a combination of the economic dispatch and the load flow problems. Thus, it seeks to find generation outputs that will minimise a cost function while simultaneously meeting not only the power balance equations but also some additional network equality and inequality constraints that represent the power systems operating limits. Therefore, minimisation of an objective function can play a key role in economically dispatching generation within power system limits. The cost function is usually defined to minimise the cost of operation but can also seek to optimise other network considerations, e.g. minimise losses. A wide variety of optimisation techniques have been applied to solving the OPF problem formulated as: nonlinear programming (NLP), quadratic programming (QP), linear programming (LP), mixed integer programming (MIP) and interior point methods [13]. These OPF techniques have their own limitations in terms of flexibility, adaptability and performance. The most successful technique is based on successive LP, which treats the problem with constraints and an objective function formulated in a linear form with only non-negative variables
This investigation uses the LP OPF engine within a commercial power system software package which has two objective functions: minimum cost and minimum control change. The controllable parameter, for the purpose of this study, is DG real power with an aim of achieving the minimum export curtailment level required to remove the thermal constraint, while respecting the priority that each DG unit has based on its connection status.
IV.
CASE STUDY NETWORKS
To meet the requirements of the AuRA-NMS thermal algorithm being a generic solution for plug and play functionality onto any distribution network the following UK networks were used for the study. The topology, voltage levels, operation and commercial agreements will differ from network to network and therefore the algorithm and OPF properties must be able to adapt to these changes. Figure I illustrates the llkV radial distribution network and Figure 2 the 33kV interconnected distribution network used for the test scenarios.
Figure I - llkV Radial Distribution Network ..- - - - - -..=..=..=.- - - - - - - - - - - - - -- - - - - - - - - - - - - -
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SUBG
[14] .
In the case of distribution networks, defining a specific objective function in the OPF problem may be more complex because DNOs do not have control over DG outputs, except when network security is threatened. This is in contrast to operation at a transmission level where the System Operator (National Grid in GB) has a set of well established rules and ancillary service arrangements to control system operation and, therefore, maintain line flows within limits. This paper investigates how an approach based on OPF can be applied for control of power flows in active distribution networks. In particular, it focuses on examining the characteristic requirements of a thermal optimal power flow management (TOPFM) technique for real time control of network thermal constraints.
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VI.
METHODOLOGY
RESULTS
This section describes the OPF formulation, the hardware and the functionality of the distributed software.
This section presents the results of the two test networks as described in the methodology section.
A.
A.
OPF Formulation
The LP OPF engine iterates between solving a full ac load flow and a primal linear programme until the optimal solution is found for the minimal cost objective function. To enable the use of this method for solving network thermal overloads, using DG real power control, the formulation must include the commercial connection agreements of the network. In this study the ' last in, first off (LIFO) principle is adopted. To achieve the above the parameters of the OPF engine that are of interest are: • • •
Line MVA inequality constraints Generator MW inequality constraints Generator MW control variables
To represent this curtailment constraint in the minimisation of the objective cost function the generators are assigned cost values for their MWh of generation. The highest cost corresponds to the last generator to connect to the network and therefore the first to be curtailed during a thermal overload event. Phase 1 - Snapshot tests The first scenarios run on the test networks were overload snapshots. The sole purpose of these were to demonstrate that commercial connection agreements were replicable through the generator MWh cost parameter of the OPF engine.
B.
C. Phase 2 - Open loop tests The TOPFM algorithm was embedded in an ABB COM600 substation computer [16] which was used in conjunction with a standard desktop computer. The desktop PC emulates data sent from measuring points on the networks and updates the network model on the COM600. Therefore, the COM600 performs the task of being a gateway to the case study networks through its OPC server. Data written to the dummy IEDs follow the IEC61850 standard and are updated in the network model, for the OPF engine, using Python functions. The TOPFM algorithm runs in a loop on the COM600 and tests the data sent from the desktop PC simulator for thermal overloads on the case network (Figure 3).
Simulator
Phase 1 Results The first round of investigative studies entailed thermal constraints being forced on the case study networks. This is used to clarify the role that MWh costing could take and hence be representative of a LIFO connection agreement. The first of these studies was carried out on the radial network with a thermal constraint placed on line I as identified in Figure l. The line is loaded to 121% of rated capacity with DG A and DG B outputting 3MW each. Table I shows the OPF solution to the line constraint with DG A having the network access rights (i.e. the cheapest MWh costs). TABLE 1 RADIAL FEEDER OVERLOAD WITH DG A HAVING PRIORITY
Generator DGA
Priority
DGB
2
I
MWOutput 3 2.35
Table 2 displays the result with the priorities reversed (i.e. DG B having the cheapest MWh costs) and it is clear that the curtailed DG unit is changed from DG B to DG A in this case. TABLE 2 RADIAL FEEDER OVERLOAD WITH DG B HAVING PRIORITY
DGA
Priority 2
DGB
I
Generator
MWOutput 2.35 3
The next thermal constraint to be evaluated was one imposed on line 2 (Figure I) which is loaded to 107.4% of its rated capacity and DG units A and B are each set to output 3MW. TABLE 3 RADIAL FEEDER OVERLOAD WITH DGA HAVING PRIORITY
Generator DGA DGB
Priority I 2
MWOutput 3 2.88
When the DG access priorities are reversed, for the above case, the result is the same as shown in Table 3. Although DG A has the higher MWh costs and should be the first generator to be curtailed it is DG B that is trimmed. This is due to DG B being the only generator behind the thermal constraint and therefore the only generator that can alleviate the overload. The next OPF thermal analysis takes place on the case study network detailed in Figure 2. Line I is subjected to a
Figure 3 - Simulator and COM600 Open-loop Test
thermal overload resulting in a line loading of 103.5% of capacity. Table 4 details the DG priority order and the unit outputs before and after the TOPFM algorithms are run. As expected the last generator to connect (Gen 5 with a priority rank of 7) is the one called upon to be curtailed and alleviate the thermal overload.
TABLE 4 INTERCONNECTED NETWORK - LINE OVERLOAD CASE
DG Gen 1 Gen2 Gen3 Gen4 GenS Gen6 Gen7
Priority 2 3 5 6 7 4 1
MW
MW
Before
After
4.35 5 12 23 23 2.4 10.2
4.35 5 12 23
TABLES INTERCONNECTED NETWORK - LINE OVERLOAD CASE
Gen 1 Gen2 Gen3 Gen4 GenS Gen6 Gen7
Priority 5 6 2 3 4 7 1
Generator DGA DGB
Priority
MW
MW
Before
After
1
1.6
1.6
2
1.985
1.91
The time taken for the OPF solution to be calculated was 0.073 seconds. Varying load and generation profiles are sent to the 33kV interconnected distribution network's COM600 to force a thermal constraint on line 1 (101.1 %), Figure 1. Table 7, below shows the connection priorities and generation output before and after the TOPFM programme was called.
14 2.4 10.2
The same line overload scenario was re-run with the generator priorities changed. Table 5 displays the results of this test case. It is noted that the generator with the least priority ranking is dispatched to zero output due to its infeed into the overloaded line. The next two generators in the priority queue (Gen 2 and Gen 1) are not trimmed or tripped as this would result in a larger power flow in the overloaded line. It is therefore the fourth generator in the priority list that is called upon to curtail its output.
Generator
TABLE 6 OPENLOOP RADIAL NETWORK TEST
MW
MW
Before
After
4.35 5 12 23 23 2.4 10.2
4.35 5 12 23 20.9 0 10.2
B. Phase 2 Results The next stage of testing the 0 PF thermal algorithm is an open loop test, as described in the methodology section. For the 11kV radial distribution network (Figure 1) varying load and generation profiles are sent from the simulator desktop PC to the COM600. Due to low load at Bus 12 and high generation output at DG B, line 3 becomes overloaded (104.4%). DG A had the priority connection (DG A = £l/MWh, DG B = £2/MWh) The OPF detection algorithm, running in a three second loop on the COM600, identifies this breach and calls the TOPFM engine to offer a solution. Table 6, shows the change in generator output (DG B) suggested by the TOPFM solution to reduce line loading to 100% of its rated capacity.
TABLE 7 OPENLOOP INTERCONNECTED NETWORK TEST
Generator Gen 1 Gen2 Gen3 Gen4 GenS Gen6 Gen7
Priority 2 3 5 6 7 4 1
MW
MW
Before
After
4.35 5 12 23
4.35 5 12 23 20.25 2.4 10.2
22.91 2.4 10.2
The OPF engine was able to find a solution, to the above thermal constraint, in 0.22 seconds.
VII. CONCLUSION AND DISCUSSION The snapshot tests, simulations and the corresponding results show that by modifying the standard formulation of the OPF problem, as described in the methodology section, it is possible to detect and solve network thermal overloads whilst adhering to a LIFO commercial connection agreement. It is worth noting that the OPF priority is to balance network power flows whilst meeting line inequality constraints. Therefore the commercial agreements are adhered to only when the network technical constraints permit. Although industrial experience proves the OPF solution is accurate through employment as a planning tool, the move to an operational implementation has some implications attached. OPF has the necessary generic characteristics in that it can cope with any network topology. In the case study investigations, it is assumed that all data necessary for load flow convergence was available. In reality the data maybe skewed or in fact erroneous. The robustness of an OPF engine relies heavily upon the delivery of 'good' data. A DNO's preference would be for robustness rather than the 'optimality' of generation dispatch close to network limits when used in an automatic control environment that is not simply for off-line decision support. In a real time and
autonomous control situation failure through divergence of the OPF engine would not be acceptable. Results from this initial study are in line with those previously published by the authors on their CSP and CT algorithms providing the authors with confidence in the techniques. Evaluation and computation time witnessed is suitably fast enough for real-time use in power flow management. VIII.
FuTURE WORK
The following gives a break down of the identified future work to enhance simulation and further evaluate the TOPFM algorithm. a.
Investigation of the risks associated with nonconvergence of the OPF engine, in a real time and autonomous environment, is required to consider the impact of the following: • • •
Model error Measurement / state estimation error Data skew
This will evaluate the robustness of the technique and direct the inclusion of some error checking or confidence functionality. b.
The intrinsic characteristic of being 'optimal' and solving close to network limits may be undesirable for a DNO due to the continual change, minute by minute, in generation output and load level. Additional functionality that can update the line inequality constraint limits could be added to build in a desirable operational safety margin.
c.
Moving to a closed loop test situation would open up some challenging questions. In what manner are new generation set points issued 'optimally' and within commercial constraints? A closed loop test would require the inclusion of the following elements: • •
•
•
d.
Thermal overload detection Sending generators 'trim/trip' control signals to alleviate the thermal overload for the current network load level Identifying when the overload is no longer present with unconstrained generator outputs and load levels Sending generators a 'run unconstrained' control signal to bring generation back onto the network
A further interesting area of investigation would be how to replicate other types of connection agreements (Le. non LIFO) through cost or minimum control functions.
e.
The analysis in this paper provides a foundation for the theoretical application of a TOPFM algorithm operating in a real time environment. For consideration in a practical environment it would be meaningful to examine genuine thermal violation cases (e.g. perhaps through contingency situations) and prove the validity of an available solution for the known overload scenario. Furthermore, an investigation of the impact of no solution being returned for a given situation needs to be addressed. REFERENCES
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