Remarks in Support of Claim 1: Adaptive Model Predictive Power Allocation Method and Apparatus for Wind–Photovoltaic–Storage Microgrids
I. Overview and Claim Characterization
Claim 1 is directed to a computer-implemented control method and corresponding apparatus configured to allocate power among multiple distributed energy resources in a wind–photovoltaic–storage microgrid. The claimed subject matter integrates: (i) adaptive model predictive control (MPC) that continuously updates plant and disturbance models; (ii) explicit handling of multi-source uncertainty encompassing renewable generation, load demand, forecasts, and communication or actuation latencies; (iii) hierarchical constraint softening through structured slack variables and penalties to guarantee feasibility while preserving safety-critical limits as hard constraints; and (iv) a real-time optimization architecture that reformulates the control problem into a convex problem with bounded complexity, enabling execution at microgrid control intervals.
II. Technical Problem and Claimed Solution
The problem addressed is the persistent conflict between (a) stochastic variability from heterogeneous sources (wind, solar, load), (b) strict device and network operating constraints (e.g., state-of-charge, inverter current, ramp-rate, and voltage/line-limit constraints), and (c) the need for tractable, real-time implementable optimization in embedded microgrid controllers. Conventional microgrid MPC schemes often assume fixed models with deterministic forecasts, adopt worst-case robust designs that induce undue conservatism, or omit a principled, hierarchical softening of constraints—leading to infeasibility during disturbances or to excessive curtailment and cost. Claim 1 resolves this by:
1) Adaptive modeling: online parameter estimation that updates a predictive model for DERs and network behavior using streaming telemetry (e.g., recursive least squares or Kalman-type estimators), thereby reducing model mismatch.
2) Multi-source uncertainty treatment: construction of uncertainty sets and/or scenario realizations capturing at least renewable output errors, load variations, and measurement/actuation delays. The predictive model is tightened via tube or chance-constrained formulations with distributional or set-based characterizations, thereby preserving feasibility without worst-case over-conservatism.
3) Constraint softening with hierarchy: explicit slack variables associated with a subset of constraints, accompanied by lexicographic or weighted penalties that enforce, in order of priority, safety and stability constraints as hard, reliability/tolerance constraints as high-penalty soft, and economic/comfort targets as lower-penalty soft. This maintains feasibility under disturbances while respecting non-negotiable electrical safety limits.
4) Real-time optimization: convex reformulation (e.g., quadratic program or second-order cone program), with move blocking, warm starts, and sparsity exploitation to ensure bounded computational complexity and deterministic solve times compatible with microgrid sampling intervals. The apparatus includes a solver engine embedded in a controller interfaced to field devices (sensors, inverters, storage systems).
III. Claim Construction and Definiteness
- “Adaptive” denotes online updating of at least one model parameter, state estimate, or disturbance characterization based on new measurements within the control horizon, as opposed to static, offline parameterization.
- “Multi-source uncertainty” includes at least contemporaneous variability and forecast error in wind and solar generation, load demand, and operational latencies/errors, each explicitly represented in the predictive optimization via uncertainty sets, scenarios, or probabilistic moments.
- “Constraint softening” means the introduction of explicit slack variables for designated constraints, included in the objective with tiered penalties to encode priority ordering; safety-critical electrical constraints (e.g., voltage, line current, and protection-related limits) are treated as hard where required by interconnection standards.
- “Real-time” signifies computational completion within the microgrid control interval (on the order of seconds or sub-seconds typical for DER dispatch), supported by the described convexity and computational strategies.
These constructions are consistent with ordinary meaning in the control and power-systems arts and render the claim definite.
IV. Novelty and Non-Obviousness
A. Novelty
The integrated combination recited in Claim 1 is not disclosed by conventional microgrid control references that either:
- employ deterministic MPC without online adaptation and without explicit multi-source uncertainty propagation;
- adopt static robust MPC using fixed worst-case bounds without hierarchical softening and thus suffer infeasibility or conservatism; or
- propose chance-constrained or scenario-based MPC lacking the claimed hierarchical slack structure aligned with power-system safety priorities and lacking the real-time convex reformulation tailored for embedded deployment.
The claimed method’s specific interplay—adaptive identification linked to uncertainty set/tube updates, hierarchical softening that distinguishes safety from performance constraints, and a solver architecture rendering the resulting program convex and executable in each sampling interval—constitutes a distinct combination of features not shown in aggregate in known approaches.
B. Non-Obviousness
Under 35 U.S.C. § 103 and the problem–solution approach under Art. 56 EPC, the claimed subject matter is non-obvious because:
- The art teaches divergent directions: robust worst-case designs that sacrifice economy, and deterministic MPC designs that risk infeasibility. Combining adaptive estimation with uncertainty-tube tightening and lexicographic softening to preserve both feasibility and performance is not a routine juxtaposition; it requires coordinated design so that estimator outputs feed uncertainty sets, which in turn dictate constraint tightening compatible with priority-based softening.
- The hierarchical slack structure produces a further technical effect: guarantees feasibility while strictly protecting safety-critical network constraints, which is non-trivial when combined with stochastic disturbances. Absent the claimed hierarchy, softening can degrade into unsafe operations or, conversely, excessive curtailment.
- The convex reformulation with move blocking and warm-start tailored to the adaptive and uncertainty-augmented structure yields predictable solve times. The art does not suggest that this multi-pronged architecture can preserve convexity and timing guarantees while handling simultaneous renewable, load, and latency uncertainties.
- The synergy between these elements goes beyond mere aggregation; adaptation reduces model mismatch so that uncertainty sets shrink, which reduces tube tightening and slack utilization, enabling a smaller convex program and improved real-time performance. This cooperative effect is neither taught nor predictable from individual components considered in isolation.
V. Enablement and Written Description
A person of ordinary skill in the art would be able to make and use the invention without undue experimentation based on the disclosure corresponding to Claim 1, including:
- System model: x(k+1) = A(θk)x(k) + B(θk)u(k) + E w(k); y(k) = Cx(k), with θk updated online via measurement-driven estimators.
- Uncertainty modeling: w(k) ∈ Wk derived from real-time forecast distributions or set-based bounds; conversion into tubes or chance constraints with specified violation budgets for non-critical constraints.
- Optimization: Over a finite horizon N, minimize J = Σk [||y(k) − yref(k)||Q^2 + ||Δu(k)||R^2] + Σi ρi||si||1/2 subject to dynamics, power balance, device/network constraints, and si ≥ 0 slack variables for designated constraints, ordered by ρ1 ≫ ρ2 ≫ … to encode hierarchy; convexity preserved by linear/quadratic costs and linear/SOCP constraints.
- Real-time implementation: move blocking for u(k), warm-starting from prior optimal sequences, and sparsity-exploiting QP/SOCP solvers on embedded hardware; controller issues setpoints to inverters and storage while acquiring field measurements.
The apparatus embodiment includes: a data acquisition interface, an adaptive estimator module, an uncertainty modeling module, a prioritization engine for hierarchical constraints, a convex optimization solver, and an actuator interface. This provides sufficient written description and enablement under 35 U.S.C. § 112(a) and Art. 83 EPC.
VI. Patent Eligibility and Technical Character
The claim is directed to a specific improvement in the operation of a microgrid—a concrete technological field—through a controller that transforms measured electrical states and forecasts into control setpoints for physical devices while respecting grid constraints. The recited features integrate a mathematical method into a specific technical process that controls energy flows in real electrical equipment, yielding a further technical effect (stable and feasible dispatch under uncertainty with bounded computation). This satisfies 35 U.S.C. § 101 and avoids exclusion under Art. 52(2)(a) EPC, as the claimed method is not a mathematical method “as such” but part of a technical control system.
VII. Industrial Applicability and Utility
The claimed method and apparatus are susceptible of industrial application in microgrids integrating wind, solar, and storage, including campus, islanded, and distribution-connected networks, providing practical utility in enhancing operational feasibility and economic efficiency under uncertainty.
VIII. Conclusion
Claim 1 recites a concrete, adaptive MPC-based control method and apparatus that simultaneously address multi-source uncertainty, constraint softening with hierarchical priorities, and real-time implementability. The claim is novel, non-obvious, enabled, patent-eligible, and industrially applicable. The combination of adaptive identification, uncertainty-aware tightening, prioritized soft constraints, and convex, real-time optimization produces a synergistic technical effect not taught or suggested by conventional microgrid control schemes.
