Ashok Krishnan

Model predictive control of a CSTR: A comparative study among linear and nonlinear model approaches

This paper presents a comparative study of two widely accepted model predictive control schemes based on mixed logical dynamical (MLD) and nonlinear modeling approaches with application to a continuous stirred tank reactor (CSTR) system. Specifically, we approximate the nonlinear behavior of a CSTR system with multiple local linear models in a MLD framework. The main benefit of such a scheme is the significant improvement in model accuracy when compared with a single linearized model. The benefits and trade-offs associated with predictive control laws synthesized using MLD and nonlinear modeling approaches are also compared.

Examining the Bernstein global optimization approach to optimal power flow problem

This work addresses a nonconvex optimal power flow problem (OPF). We introduce a ‘new approach’ in the context of OPF problem based on the Bernstein polynomials. The applicability of the approach is studied on a real-world 3-bus power system. The numerical results obtained with this new approach for a 3-bus system reveal a satisfactory improvement in terms of optimality. The results are found to be competent with generic global optimization solvers BARON and COUENNE.

Multi-Energy Scheduling Using a Hybrid Systems Approach

Abstract This paper presents a mixed logical dynamical (MLD) approach for modelling a multi-energy system. The electrical and thermal energy streams are linked through the operation of combined cycle power plants (CCPPs). The MLD approach is used to develop detailed models of the gas turbines (GTs), steam turbines (STs) and boilers. The power trajectories followed by the GTs, STs and boilers during various start-up methods are also modelled. The utility of the developed model is demonstrated by formulating and solving an optimal scheduling problem to satisfy both electrical and thermal loads in the system. The cost benefit of including flexible loads in the scheduling problem formulation is demonstrated through suitable case studies.

Optimal scheduling of a microgrid including pump scheduling and network constraints

This paper proposes an efficient energy management system (EMS) for industrial microgrids (MGs). Many industries deploy large pumps for their processes. Oftentimes, such pumps are operated during hours of peak electricity prices. A lot of industries use a mix of captive generation and imported utility electricity to meet their energy requirements. The MG considered in this paper includes diesel generators, battery energy storage systems, renewable energy sources, flexible loads, and interruptible loads. Pump loads found in shipyard dry docks are modelled as exemplar flexible industrial loads. The proposed EMS has a two-stage architecture. An optimal MG scheduling problem including pump scheduling and curtailment of interruptible loads (ILs) is formulated and solved in the first stage. An optimal power flow problem is solved in the second stage to verify the feasibility of the MG schedule with the network constraints. An iterative procedure is used to coordinate the two EMS stages. Multiple case studies are used to demonstrate the utility of the proposed EMS. The case studies highlight the efficacy of load management strategies such as pump scheduling and curtailment of ILs in reducing the total electricity cost of the MG.

Application of partitioned-based moving horizon estimation in power system state estimation

The Partitioned-based Moving Horizon Estimation (PMHE), developed previously by others, is applied to the power system state estimation problem in this paper. The constraints on state variables and noises are taken into account in this scheme. In this distributed approach, the network is partitioned into several non-overlapping and observable areas. The global Jacobian matrix is required during the initial time before approaching the converged states. Only the estimated information data between neighboring areas are exchanged afterwards. The communication traffic is thus significantly reduced compared to a centralized solution. Meanwhile, each area estimates its local states by solving a smaller size optimization problem. The optimization problem is, therefore, scalable. PMHE converges to the centralized solution of moving horizon estimation (MHE) within finite time steps. Numerical simulation with the IEEE 14-bus system shows the convergence of PMHE. Further, the estimated states are better than those from the weighted least squares (WLS) with large outliers.