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Real Time Approach for Load Balancing in Virtual
Power Plant Structures
Ciprian Lupu, Dumitru Oancea, Cristian Oară
Faculty of Automatic Control and Computers, University
Politehnica of Bucharest, Romania
[ciprian.lupu, cristian.oara]@acse.pub.ro ,
Mircea Lupu
CM. AOSR, Transilvania University of Brasov,
Brasov, Romania
Dan Apetrei
Elsaco Energy S.R.L., ELSACO
Bucharest, Romania
Abstract — A novel real time approach structure
implementation to manage multiple sources and/or generator
systems on maintaining the global load, in case of charge and
functioning disturbances is proposed. Its applicability is
validated on a control structure of two and four constant and
variable load sources, connected in parallel to provide energy, a
situation usually encountered these days in the renewable energy
industry (micro hydropower plant, solar, wind and small
generators), Virtual Power Plant (VPP), or micro/smart grid.
Keywords—real time; balancing; control architectures;
disturbance rejection; renewable energy;
I. INTRODUCTION
Renewable sources are notoriously known to need special management for connecting to the existing distribution network, to cater for their output unpredictability, and hence for the introduced disturbances.
The renewable energy sources most frequently used are: solar (photovoltaic (PV) equipments), wind (eolian farms), and water ((micro) hydropower plants). For production of large quantities of energy, needed e.g. for small towns or residential areas, these sources are grouped together in plants situated in geographical areas with energy potential.
Unfortunately, for the time being these (multiple) systems (e.g. solar and wind) have a discontinuous functioning as they are unavoidably dependent on sunlight and wind blow. Therefore, the production of energy needs to be load balanced using some alternative sources (e.g. hydropower plant and, if possible, different energy storage elements) to assure a global required output. To optimize in real time several systems may be used to build a balanced control structure.
The balanced control of multiple sources/generators was and continues to be an important subject in the area of practical applications. Various solutions, from simple ones like parallel marking of control loops [1-3], to complex structures and systems using e.g. VPP [4], have been implemented and/or just theoretically proposed. The purpose
of these solutions is certainly to increase performance, quality and secure exploitation of production installation. For several works in this area see for example [1-6].
To ensure the stability of a multiple generator structure, a solution making use of a storage system that accumulates the surplus energy and reconverts it when needed [6] is requested. Stopping one of the sources can lead to the necessity of compensating the system with another one or with a storage power plant. At the same time, the micro hydropower plant can be treated both as a storage and an active element.
To store energy temporarily one can implement alternatively a system with a pumped storage power plant (electricity is transformed into a mechanical energy, e.g. by pumping the water through pipes into reservoirs), one with a hydrogen storage obtained through electrolysis [7], or simply a battery.
The main subject of this paper is the Real Time – Balanced Control Structure (RT-BCS), in which the load of some sources needs to vary in order to maintain a precise global output, despite the transitory regime of the set-point, and/ or load disturbances that might occur on the plants.
This mechanism is one of the main components of modern VPP structures [4], and can be applied in a variety of industrial processes such as local/zonal energy generation.
This paper tries to offer high performance solutions in which the local set-point itself is modified during the operation (real time). The operations should be developed as fast as possible, while assuring the highest level of precision in the transitory phase that follows the adjustment.
A typical case when the set point of a parallel system needs to be raised in order to maintain a constant output value appears when a source becomes inactive, or is damaged, and another source needs to compensate with extra power (see Figure 1).
In this figure E1 and E2 are eolian generators, PV1, PV2, and PV3 are photo-voltaic cells, MHP1 is a micro
Fig. 1 System with multiple energy sources in VPP
Fig. 2 A classic solution based on parallel connection of
two or more sources without compensation [8]
hydro power plant, L1 to LN are loads and SW-ACC is a load balancing structure (possibly with accumulators).
The tested scenarios cover the main critical situations encountered in real exploitation and include perturbation of control loops, load set points adjustments and compensating the failure/limitation of a source.
II. EXISTING SOLUTIONS
A. A classic solution
Parallel control structures for Multiple-Input Multiple-Output (MIMO) systems were used for a long time and they proved success in different industrial processes [1], [3], [8]. One such structure is presented in Figure 2.
As documented in [6], [8], few experiments/simulations have been developed on this structure of two parallel sources (processes). The main idea here is to produce a certain quantity of energy without employing a global compensation method.
The set point r for the global parallel system is additively decomposed in two set points (r=r1+r2), one for each subsystem, while the corresponding outputs are y1 and y2, respectively. Denoting
, , 22
11
r
ra
r
ra
we have
121 aa
The global error is calculated using the obvious formula:
iyiyirie 21
NsNs
ieiERRaieiERRs00
,
where i=0, 1, …, Ns, and Ns is the number of samples.
According to [5], the integral (signed) global error (ERRs) for parallel systems is not 0. If one generator fails, there is no possibility to compensate it.
In many real-life cases, normal plants have no continuous control elements for load (e.g. fixed load pumps etc.), or the variation domain of their control elements is limited. In either case, the overall load control is very difficult.
B. An alternative solution
A better parallel control scheme for MIMO systems is given in [6], (see Figure 3 below). Here, the main idea is to add a fraction of the control error as a supplementary set point value to a neighbor (parallel) system/generator, i.e.,
2122
1211
Fearr
Fearr
where F1 and F2 are transfer functions or just simple constants having a twofold role: to normalize the set point for every generator, and to stabilize the system, preventing auto-oscillation. For a1 and a2 the same condition (2) is imposed or, more general, considering a number of generators N>2,
N..., 2, 1,=i where,1 ia
The output of this multiple generator system is given by
N ..., 2, 1,=i where,1N
iyy
In the sequel we will call this as a cross-compensation solution.
The use of a load balancing structure can compensate disturbances and, theoretically, the failure or limitation of one generator. In fact, compensating the failure of one generator impose an important “reserve” (e.g. at least 50% for two element structures, see for example [5]). Actually, the bigger the number of parallel generators ensures the easier to support the shutting down of any of them.
In normal operation, null error actually means a null supplementary set point adjustment.
Fig. 3 A cross-compensation solution for a parallel
connection of two sources with balanced structure [6]
Fig. 4 A novel solution for parallel connection of two
sources with balanced structure
Fig. 5 The proposed solution with supplementary filtering
elements
Compared with the parallel scheme (Figure 2), the structure presented in Figure 3 has an additional degree of adaptation, given by the variables F1 and F2. Apart from their already mentioned roles, these variables can ensure a lower value for the ERRa parameter as well.
However, finding the values for the blocks F1 and F2 is often difficult, especially in the case of multiple generators, and therefore the experimental route is customary. This particular feature actually limits the use of the cross-compensation structure.
III. A NOVEL LOAD BALANCING STRUCTURE
A. Basic Structure
The effort to design the components is improved in the structure proposed in Figure 4. Here, the main element is the controller, denoted C, which acts for the general set point balance. The requested load, represented by r is compared with the global output y and, accordingly, rc – the (re) calculated set point - is adjusted. In particular, the controller C can be designed by using various classical algorithms, e.g. PI, PID, RST etc.
As it is shown in the next section, if one of the generators fails or has limited operation regime, the action of the controller C will increase the (re)calculated set point value rc, such that the generated load value y is reached and maintained constant.
Notwithstanding, in real-time operation the difference between the time constants of the processes can lead to oscillatory regimes. To avoid this, some filter type elements should be included leading to the augmented structure in Figure 5.
B. Supplementary elements
Fr and Fy are (digital/software implemented) filters for set point (reference) and for global measured load (y), respectively. The structural combination of C, Fr and Fy suggests the use of a RST control algorithm with two degrees of freedom [11], featuring superior performance.
However, an RST controller based solution is usually recommended when the model of the process is well known.
C. Constant elements
In certain circumstances, in particular for old generators, the load variation is reduced and can be considered relatively constant. Furthemore, a PV element has similar (constant) behavior in full sunny days [9]. For these situation RT-BCS must manage a hybrid constant-variable group and the load balancing is limited, depending on the type of elements. Experimental tests given in next section include this situation.
IV. EXPERIMENTAL TESTS
For validating the proposed system performance several tests have been implemented in Matlab – Simulink, and various evolution scenarios have been imagined. An example of implemented Simulink structure is presented in Figure 11. For all experiments supplementary filtering elements have been considered.
A discussion comprehending the classic and the cross-compensation solutions in Figure 2 and Figure 3, respectively is presented in [6], proving the advantages and superiority of the latter. In the present paper no additional arguments are brought alongside this comparison.
A. The cross-compensation [6] vs. the novel solution
In the first instance, Matlab/Simulink experiments describe comparative evolutions of the cross-compensation [6] and the proposed novel solution. Here, for each structure, the same two continuous close loops (generator process and corresponding controller) are considered.
For the generators consider a variable load between 1 and 1.5 (these can be considered fast generators, having 1-1.5 MW power). The imposed load is distributed 65% for the first generator (more powerful) and 35% for the second one (a1 = 0.65, a2 = 0.35). The F1 and F2 filters (see Figure 3) are considered 1 (no action), while Fr and Fy (see Figure 5) are considered first order systems (low pass filters).
Fig. 6 Cross-compensation (up), vs. proposed structure
(down) testing evolution
Fig. 7 ERRas evolutions
Cross-compensation (up) vs. the proposed novel structure
(down)
Fig. 8 Testing evolution for four parallel systems
Cross-compensation (up), vs. the proposed novel structure
(down)
As shown in Figure 6, the considered group must start with an imposed trajectory and raise 1 (MW) power. On the sampling time moment equal to 150, the imposed load increase is 1.5 MW.
As disturbance tests, consider the system 2 is affected at time moment equal to 75, and fails at time moment equal to 230. Under these circumstances, the load balanced structure reacts and increases system 1 load to ensure the global result. One can easily see that both structures ensure correct operation, with generally similar performances. However, the proposed novel structure ensures superior performance starting on time moment 230 (generator 2 fails). This superiority can be better depicted by looking at the corresponding ERRas given in Figure 7.
For the cross-compensation and the proposed novel structures the ERRas are close to 0.9 (MW) (up) and 0.634 (down), respectively, which means a 29% improvement. If the deviations have certain financial weight (deviations are always
penalized), the obtained improvement can be directly quantified in money.
Actually, the first experiments are a little bit too drastic since there are no generators operating with a 50% power reserve as this would imply a completely non-economical regime. However, the two generators case is a powerful test about the structure stability and performance. Clearly, more (than two) generators/sources/elements request lower load compensation per each item/element.
B. Four parallel elements
Four continuous closed loops (generator process and corresponding controller) with the same (model and PID) corresponding parameters are considered. For this second series of experiments, the comparative evolutions of cross-compensation [6] and the proposed novel solutions are given.
For the generators a variable load between 1 and 1.5 (these can be considered fast generators, having 0.7-1.5 MW power). The imposed load is distributed 35% for the first generator, 30% for the second, 20% for the third, and 15% for the fourth one (a1 = 0.35, a2 = 0.30, a3 = 0.20, a4 = 0.15). The F1, F2, F3, F4 filters (see Figure 3 updated for four elements) are considered 1 (no action), while Fr and Fy (see Figure 5 updated for four elements) are considered first order systems (low pass filters).
For the set point tracking consider the same scenario like for the first experiments in subsection A. As disturbance tests, consider the system 2 is affected at time moment equal to 75, and fails at time moment equal to 230 (see Figure 8); further, at time moment 300 and 350 the third and fourth systems, respectively, are disturbed. Under these circumstances, the load balanced structure reacts to ensure the global load.
In this experiment both structure have similar curve evolutions (Figure 8). The subtle differences are counted in ERRa parameter equal to 0.46 (KW) for the first structure [6] and 0.36 for the proposed novel one, representing about 21% better performance.
Fig. 9 ERRa evolution for four systems
Cross-compensation (up) vs. the proposed novel structure
(down)
Fig. 10 Load balancing for hybrid (constant-continuous)
load; general evolution (up), C controller output (middle),
ERRa (down) evolutions
The superior performance of the proposed novel structure may be explained by the tuning accessibility of the controller C (compared to F1 - F4 filters design); the tuning procedure may span a whole variety of techniques from classic PID, pole placement, to more sophisticated norm-optimal ones.
C. Structure with “constant” elements (Hybrid structure)
As stated at the end of Section III, in several circumstances the load variation might be reduced and the load itself could be considered relatively constant. The RT-BCS structure was tested for this situation with three constant loads (1, 2, 3) and one continuous variation generator (4). The constant elements could be the PV area while the continuous element could be a
micro-hydro generator (small dam) or other continuous (bio-gas) plant.
Here, the main element is the continuous generator since without it a precise set point tracking would not be possible.
The experiment scenario assumes, after the imposed start, a set point increase from 1 to 1.5 (MW), at time moment equal to 150, followed by a decrease to 1 (MW) at time moment equal to 300. From the disturbance point of view, the system 2 is affected at time moment 75 and fails at time moment equal to 230.
During these events, system 4 (in green color) has some important variations to enforce an error as small as possible.
System 3 has an interesting evolution: at the beginning, it starts for a small time period to complement system 4 efforts and, further, it starts again when the set point increases. When the set point decreases, it simply stops.
The output of the controller C is presented in the middle graphic (in Figure 10): system 4 has a similar shape with respect to the evolution of controller output (because C controller determines (re)calculation of general set point and only system 4 has continuously load evolution).
On this hybrid structure ERRa has an increased value since the constant generators have a lower flexibility comparative to all continuous elements. In fact, without the continuous element, the structure could have an oscillatory behavior.
V. CONCLUSIONS
The proposed real time balancing structure is suitable to compensate for one or more disturbed, failed or limited components in MIMO parallel systems, while it can ensure superior performance versus the classical or cross-compensation parallel solutions.
A main application of the novel re-balancing approach proposed here is in VPPs containing multiple renewable energy sources (wind, solar, hydro, thermal etc.), in which the evolution of the weather represents the main disturbance.
In all tests, classic PI-PID control algorithms were used (see for example the simulated structure in Figure 11). Two degree of freedom controllers (e.g. RST [11]) are recommended for achieving superior performance (reference tracking, disturbance rejection, etc.).
Moreover, the re-balancing structure is quite simple and suitable for real time implementation constraints (see for example [10]). Depending on the process structure and implemented peculiarities, the re-balancing structure could be used in upper or safety levels.
Acknowledgment
This work was co-financed from University Politehnica of Bucharest – A.C.P.C. Research Center and projects from Automatics and Computers Science Faculty. The work of C. Oara was supported by a grant of the Romanian National Authority for Scientific Research, CNCS - UEFISCDI, project identification number PN-II-ID-PCE-2011-3-0235.
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Fig. 11. Implemented Matlab-Simulink simulated structure
