Power Systems Optimization Engineer

Electric Power Engineers (EPE) is hiring a Power Systems Optimization Engineer for its software team. The role develops and productionizes optimization methods for grid planning, including selecting cost-effective mitigation portfolios and evaluating their economic value. The engineer collaborates with planners, developers, and product owners to integrate reliable optimization tools into EPE’s planning platform.

Responsibilities:

• Formulate and solve problems that select least-cost combinations of transmission upgrades, storage, reconfiguration, and other mitigations to resolve thermal, voltage, and stability violations across multiple cases, seasons, and contingencies
• Build methods for cost allocation, benefit-cost and production-cost-informed evaluation, and comparison of alternative portfolios to support planning and interconnection decisions
• Develop MILP/MINLP formulations, AC/DC power flow relaxations and linearizations (e.g., DC/PTDF, LODF, SOCP), decomposition approaches (e.g., Benders), and heuristics that scale to large real-world systems
• Prototype in Python, validate against full AC power flow results from industry tools, and work with developers to deploy methods as robust, scalable services
• Verify that optimized portfolios solve the violations when re-simulated; quantify optimality gaps, runtime, and sensitivity to assumptions
• Partner with transmission planners to capture real-world constraints (lead times, outage feasibility, cost estimates, planning criteria) and translate them into model constraints and objectives
• Explain optimization results, trade-offs, and assumptions to engineers, clients, and stakeholders; support onboarding and tailored deployments
• Write methodology papers, model formulations, and validation reports that help engineers and clients trust the results

Requirements:

• Master's degree or PhD in Electrical Engineering, Operations Research, Applied Mathematics, or a related field, with a focus on power systems and/or optimization
• A Bachelor's with equivalent experience will be considered
• 3+ years of experience applying optimization to power systems problems (e.g., transmission expansion planning, optimal power flow, unit commitment, resource or portfolio planning), in industry or research
• Strong foundation in mathematical optimization: linear, mixed-integer, and non-linear programming, convex relaxations, and decomposition methods
• Proficiency in Python and at least one optimization modeling framework (e.g., Pyomo, JuMP, CVXPY, PuLP, gurobipy) and commercial or open-source solvers (e.g., Gurobi, CPLEX, Xpress, HiGHS, Ipopt)
• Solid understanding of power flow, contingency analysis, and transmission planning concepts, including the US transmission system and ISO/RTO structures
• Strong communication skills: able to explain complex models, assumptions, and results to engineers and non-technical audiences
• Excellent problem-solving skills, especially in diagnosing infeasibility, numerical issues, and scaling challenges in large models
• Hands-on transmission planning or interconnection study experience (steady-state, short circuit, dynamic stability) using tools such as PSS/E, PSLF, PowerWorld, TSAT, or TARA
• Experience with transmission expansion planning, network upgrade cost allocation, or interconnection cluster studies (e.g., SPP DISIS, MISO DPP, PJM cycle studies)
• Experience with production cost or capacity expansion models (e.g., PLEXOS, PROMOD, GridView, Aurora, PowerSIMM) and economic planning metrics (APC, adjusted production cost, benefit-cost ratios)
• Experience with stochastic or robust optimization and scenario-based planning under uncertainty
• Familiarity with power systems optimization packages (e.g., PowerModels.jl, pandapower, PyPSA, MATPOWER)
• Software engineering practices: Git, testing (pytest), code review, and deploying models in cloud or distributed environments
• Knowledge of machine learning methods used to accelerate optimization (e.g., surrogate models, warm-starting, constraint screening)
• Publications or open-source contributions in power systems optimization