Chapter III: Load-Side Frequency Control in Multi-Machine Networks
3.2 Load control and system dynamics as primal-dual algorithm
Remark3.3. Note that (3.7b) does not require the balance of generation and load at each individual bus, but only balance across the entire network. This constraint is less restrictive and offers more opportunity to minimize costs. Additional constraints can be imposed if it is desirable that certain buses, e.g., in the same control area, rebalance their own supply and demand, for economic or regulatory reasons.
We make the following assumption regarding OLC (3.7).
Assumption3.1. OLC (3.7) is feasible. The cost functionscjare strictly convex and twice continuously differentiable on f
dj,dj
g.
See Remark 2.2 for examples of practical load control cost functions that satisfy Assumption 3.1.
3.2 Load control and system dynamics as primal-dual algorithm
The following two results are proved in Appendices 3.C and 3.D. Instead of solving OLC directly, they suggest solving DOLC and recovering the unique optimal point (d∗,dˆ∗)of OLC from the unique dual optimalν∗.
Lemma3.1. The objective functionΦof DOLC is strictly concave overR|N |. Lemma3.2. 1. DOLC has a unique optimal point ν∗ with νi∗ = ν∗j = ν∗ for all
i, j ∈ N.3
2. OLC has a unique optimal point (d∗,dˆ∗)where d∗j = dj(ν∗)and ˆd∗j = Djν∗ for all j ∈ N.
To derive a distributed solution for DOLC, consider its Lagrangian L(ν, π) := X
j∈N
Φj(νj)− X
(i,j)∈E
πi j(νi−νj), (3.11)
where ν ∈ R|N | is the (vector) variable for DOLC and π ∈ R|E | is the associated dual variable for the dual of DOLC. Henceπi j, for all(i,j) ∈ E, measure the cost of not synchronizing the variablesνi andνj across busesi and j. Using (3.8)–(3.11) a partial primal-dual algorithm for DOLC takes the form
ν˙j = γj
∂L
∂νj
(ν, π) =−γj
dj(νj)+Djνj−Pmj +πoutj −πinj
(3.12a) for j ∈ G,
0 = ∂L
∂νj
(ν, π)= −
dj(νj)+ Djνj −Pmj +πoutj −πinj
for j ∈ L,(3.12b) π˙i j = −ξi j
∂L
∂πi j
(ν, π) = ξi j(νi−νj) for(i, j) ∈ E, (3.12c) whereγj > 0, ξi j > 0 are stepsizes and πoutj := P
k:j→kπj k, πinj := P
i:i→jπi j. We interpret (3.12) as an algorithm iterating on the primal variablesνand dual variables π over timet ≥ 0. Set the stepsizes to be:
γj = M−1j , ξi j = Bi j.
Then (3.12) becomes identical to (3.6a)–(3.6c) if we identifyν withω and π with P, and use dj(ωj) defined by (3.9) for dj in (3.6a)–(3.6b). This means that the frequency deviationsωand the branch flowsPare respectively the primal and dual variables of DOLC, and the network dynamics, together with frequency-based load control, execute a primal-dual algorithm for solving DOLC.
3For simplicity, we abuse the notation and useν∗to denote both the vector
ν∗j,j ∈ N and the common value of its components. Its meaning should be clear from the context.
Remark3.4. Note the consistency of units between the following pairs of quantities:
1)γjandM−1j , 2)ξi j andBi j, 3) νandω, 4)πandP. Indeed, since the unit ofDjis [watt·s] from (3.6a), the cost (3.7a) is in f
watt·s−1g
. From (3.8) and (3.11),νand πare respectively in f
s−1g
(or equivalently f
rad·s−1g
) and [watt]. From (3.12a),γj
is in f
watt−1·s−2g
which is the same as the unit ofMj−1from (3.6a). From (3.12c), ξi j is in [watt] which is the same as the unit ofBi j from (3.6c).
For convenience, we collect here the system dynamics and load control equations:
ω˙j = − 1 Mj
dj+dˆj −Pmj +Poutj −Pinj
for all j ∈ G (3.13a) 0 = dj+dˆj−Pmj +Poutj −Pinj for all j ∈ L (3.13b) P˙i j = Bi j
ωi−ωj
for all(i, j) ∈ E (3.13c)
dˆj = Djωj for all j ∈ N (3.13d)
dj = f
c0j−1(ωj)gdj
dj for all j ∈ N. (3.13e)
The dynamics (3.13a)–(3.13d) are automatically carried out by the power system while the active control (3.13e) needs to be implemented at each controllable load.
Let(d(t),dˆ(t), ω(t),P(t))denote a trajectory of (deviations of) controllable loads, frequency-sensitive loads, frequencies and branch flows generated by the dynamics (3.13) of the load-controlled system.
Theorem 3.1. Starting from any feasible4 initial point (d(0),d(0), ω(0),ˆ P(0)), every trajectory (d(t),d(tˆ ), ω(t),P(t),t ≥ 0) generated by (3.13) converges to a limit(d∗,dˆ∗, ω∗,P∗)ast → ∞such that
1. (d∗,dˆ∗) is the unique vector of optimal load control for OLC;
2. ω∗is the unique vector of optimal frequency deviations for DOLC;
3. P∗is a vector of optimal branch flows for the dual of DOLC.
We will prove Theorem 3.1 and other results in Section 3.3.
Implications
Our main results have several important implications:
4A point is feasible for (3.13) if it satisfies the algebraic equations (3.13b), (3.13d), and (3.13e).
1. Ubiquitous continuous load-side primary frequency control. Like the generator- side droop control, frequency-adaptive loads can rebalance power and resyn- chronize frequencies after a disturbance. Theorem 3.1 implies that a multi- machine network under such control is globally asymptotically stable. The load-side control is often faster because of the larger time constants asso- ciated with valves and prime movers on the generator side. Furthermore OLC explicitly optimizes the aggregate disutility using the cost functions of heterogeneous loads.
2. Complete decentralization. The local frequency deviationsωj(t)at each bus convey exactly the right information about global power imbalance for the loads to make local decisions that turn out to be globally optimal. This allows a completely decentralized solution without explicit communication among the buses.
3. Equilibrium frequency. The frequency deviations ωj(t) at all the buses are synchronized to ω∗ at optimality even though they can be different during transient. However ω∗ at optimality is in general nonzero, implying that the new common frequency may be different from the common frequency before the disturbance. Mechanisms such as isochronous generators [15] or automatic generation control are needed to drive the new system frequency to its nominal value, usually through integral action on the frequency deviations.
We develop distributed control schemes to restore frequency to its nominal value, in Sections 3.5, 3.6, and 3.7.
4. Frequency and branch flows. In the context of optimal load control, the frequency deviationsωj(t) emerge as the Lagrange multipliers of OLC that measure the cost of power imbalance, whereas the branch flow deviations Pi j(t) emerge as the Lagrange multipliers of DOLC that measure the cost of frequency asynchronism.
5. Uniqueness of solution. Lemma 3.2 implies that the optimal frequency devi- ationω∗ is unique and hence the optimal load control (d∗,dˆ∗) is unique. As shown below, the vector P∗ of optimal branch flows is unique if and only if the network is a tree. Nonetheless Theorem 3.1 says that, even for a mesh network, any trajectory of branch flows indeed converges to a limit point. See Remark 3.6 for further discussion.