Distributed control of DGs within microgrid

In this blog we continue our overview on the use of M2M communications in microgrids (see https://massm2m.wordpress.com/2012/09/11/m2m-communications-in-microgrids/).

As outlined in the previous blog, the future smartgrid is envisioned as a network of microgrids. Microgrid is a small-scale electrical network consisting of localized distributed generators, storage capacities and loads, interconnected by low-voltage (LV) cables.

Distributed generators (DGs) within microgrid typically exploit renewable energy sources (such are photo-voltaic and wind generators) whose behavior cannot be controlled, posing new challenges with respect to balancing the generated power and user loads. Basically, when controlling the DGs within microgrid, the imperative is that their output voltages and frequencies should match.

In traditional grids, the voltage and frequency outputs of generators are regulated using the so-called droop control. Droop-control is essentially a proportional control algorithm that is executed locally at each generator, driving output voltage and frequency based on the locally measured active and reactive powers. Loads and generators in traditional grids are interconnected using high-voltage cables, which have much greater reactance then resistance. Taking into account this fact, it can be shown that, when the active and reactive power that are flowing into the cable from the generator are expressed through the generator’s voltage (and assuming that the power angle is small), the active power depends on the generator’s frequency and the reactive power depends on the generator’s voltage. The overall result is that, in traditional droop control, the frequency is regulated by the local measurements of the active power, and voltage is regulated by the measurements of the reactive power.

Traditional droop control cannot be used directly in AC microgrids[*], as resistance of LV cables dominates over reactance. One way to address this issue is to modify the droop control such that control algorithm includes the information exchanged among DGs[**] using communication network.

In a recent paper [1], the authors apply the paradigm of distributed consensus algorithms, exploited in their earlier works (see previous blog, https://massm2m.wordpress.com/2012/09/11/m2m-communications-in-microgrids/), to conduct series of local exchanges among DGs, in order for all DGs to learn what is the global state of active and reactive power. Traditional droop control is then augmented to include this information, such that frequency controller includes a term proportional to the differences between the desired and the actual active and reactive power, and the voltage controller uses a corresponding integral term. Using small signal analysis, the authors show that improved stability of the microgrid is achieved with respect to the traditional drop control.

The authors of [2] consider a similar setting, but the focus of their work is the impact of communication impairments, such are packet loss and delay, on the (modified) droop control, rather than on the control algorithm itself. The key observation is that outdated information about remote measurements, when combined with locally obtained measurements, can adversely affect the performance of the droop control. In order to neutralize this effect, the authors suggest a scheme in which locally obtained signal is delayed using the same delay distribution of the remote signal. The local delay is realized using Smith predictor and it is statistically the same as the delay of the remote signal. Finally, the authors present the simulation results obtained in an example microgrid with two DGs, showing that in the proposed scheme a more stable active power output is achieved, compared to the case when there is no local delay.

Recently, our group started collaboration on the same topic with a group at Department of Energy Technology at our university, with an aim of designing of robust and simple communication algorithms in the framework of DG control, both in AC and DC microgrids. The initial results are encouraging, however, we leave their presentation for our future blogs.

[1] H. Liang et al, “Decentralized Inverter Control in Microgrids Based on Power Sharing Information through Wireless Communications”, to be presented at IEEE GLOBECOM 2012

[2] S. Ci et al, “Impact of Wireless Communication Delay on Load Sharing Among Distributed Generation Systems Through Smart Microgrids”, IEEE Wireless Communications, June 2012

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[*] Regulation of DGs in DC microgrids will be addressed in our future blogs.

[**] Droop control is actually applied at inverters that connect DGs to the interconnecting AC bus.

M2M communications in microgrids

As traditional energy resources are becoming increasingly scarce and thus more expensive (not to mention their adverse effects on the environment), the efforts towards energy generation from alternative, environmentally friendly resources and smart energy consumption are rapidly becoming concern of governments, industry and academia all over the world. The recent buzz-term smart grid symbolizes this important concept of the efficient generation, distribution and exploitation in case of electrical energy. In brief, smart grid comprises all elements of the electrical grid, but it is much more than just a collection of its parts – it is a dynamic system that interconnects them in a meaningful manner for the benefit of the end users. As such, smart grid heavily relies on communications between consumers, suppliers, smart devices and applications.

Smart grids of the future are envisioned as networks of integrated microgrids – (geographically) localized collections of generators, storage capacities and users (loads) that operate as singly entity with the goal of smart energy exploitation. With respect to this, the centralized approach for the regulation (i.e., control) of microgrid operation imposes itself naturally. However, the decentralized, distributed approach for microgrid control (known as the paradigm of multiagent systems) is a more favorable one, as it is inherently more robust and scalable. Naturally, distributed control should leverage on adequate distributed communication techniques.

A recent issue of IEEE Wireless Communications (Jun 2012), under the topic “Recent advances in wireless technologies for smart grid”, presents a couple of research articles devoted to the wireless communications within microgrid. Particularly, a generalized approach for the multiagent coordination is assessed in [1], investigating the usage of consensus algorithms for distributed dissemination of information among the agents. The idea behind consensus algorithms is a simple one – series of local exchanges among the agents should result with each agent having the same insight into the global network parameters (in other words, communicating only locally spreads information globally – a well-known phenomenon in the social networks). In turn, this allows agents to separately apply the control algorithms using the same data, harmonizing their operation to achieve the optimal performance of microgrid. As the actual communication technology, the authors suggest use of WiFi (IEEE 802.11) or ZigBee (IEEE 802.15.4). The above generalized approach can be applied to a number of issues in smart grid operation, such are:

  • Economic dispatch problem,
  • Decentralized invertor problem,
  • Fault detection and recovery,
  • Agent clock synchronization (when GPS is not used),…

Economic dispatch

A related article [2] gives a nice analysis of the economic dispatch problem when the impact of communications is included. Economic dispatch [3] is a short-term determination of the optimal output of a number of electricity generation facilities to meet the system load at the lowest possible cost, while serving power to the public in a robust and reliable manner. The authors of [2] develop a plausible cost model which, besides generation costs, cost of purchasing power and cost of generating extra power, includes both the communication costs and the impact of communication errors on the possible deviation of system operation from the optimum. It is shown that the use of distributed consensus algorithms can in general steer the system operation towards the optimum. Further, it is shown that use of communication links that interconnect distant agents (which are not directly connected by WiFi/Zigbee links) can speed up the convergence to the desired state of optimal operation, lowering the total cost. On the other hand, these “global” links are provided using cellular communications, which increases communications costs and thus increases the total costs; the overall result being that the amount of cellular link usage should be carefully selected.

Although these initial research efforts are promising, a lot remains to be done. The impact of wireless communications impairments on microgrid operation is still not adequately addressed nor experimentally verified. With respect to the latter, it remains to be seen how effectively will WiFi/ZigBee networks (which are almost exclusively operating in a star/tree topology) support the execution of the distributed consensus algorithms.

[1] H. Liang et al, “Multiagent Coordination in Microgrids via Wireless Networks”, IEEE Wireless Communications, Jun 2012

[2] H. Liang et al, “Decentralized Economic Dispatch in Microgrids via Heterogeneous Wireless Networks”, IEEE Journal on Selected Areas In Communications, July 2012

[3] http://en.wikipedia.org/wiki/Economic_dispatch

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