Energy System Optimization & Simulation

Lesson 1 • Energy Balance and Accounting

Constructs input-output energy balances for subsystems and whole plants. Provides the quantitative foundation for optimisation objectives.

Lesson 2 • Key Performance Indicators

Introduces efficiency, capacity factor, heat rate, and availability metrics. Links KPIs to optimisation targets defined in later chapters.

Lesson 3 • Thermodynamic Principles Review

Applies first and second law concepts to energy conversion devices. Connects thermodynamic limits to real-world efficiency targets.

Lesson 4 • Energy System Taxonomy

Defines generation, transmission, distribution, and end-use layers. Establishes vocabulary used throughout the course.

Lesson 1 • Dynamic and Transient Modelling

Extends steady-state models with differential equations for time-varying behaviour. Prepares models for dynamic simulation in subsequent chapters.

Lesson 2 • Component-Level Modelling

Derives governing equations for generators, heat exchangers, and storage units. Builds reusable component blocks for system assembly.

Lesson 3 • Model Validation Techniques

Tests model predictions against measured data using statistical error metrics. Establishes confidence levels before optimisation use.

Lesson 4 • System-Level Integration

Assembles component models into a coupled system using nodal and loop equations. Demonstrates how interactions propagate across subsystems.

Lesson 1 • Scenario and Sensitivity Runs

Automates parameter sweeps and scenario comparisons across multiple simulation runs. Builds the analytical foundation for optimisation in later chapters.

Lesson 2 • Dynamic Simulation Execution

Simulates load-following, startup, and fault scenarios over time horizons. Connects dynamic outputs to KPI evaluation from Chapter 1.

Lesson 3 • Simulation Documentation Standards

Establishes version control, metadata logging, and reproducibility practices for simulation projects. Supports audit trails required in professional settings.

Lesson 4 • Simulation Environment Setup

Configures solvers, time steps, and numerical tolerances for energy simulations. Correct setup prevents convergence failures in later exercises.

Lesson 1 • Mixed-Integer Programming

Handles unit commitment and on/off decisions using MIP formulations. Addresses computational complexity and relaxation strategies.

Lesson 2 • Optimisation Problem Formulation

Defines objective functions, decision variables, and constraints for energy systems. Correct formulation is prerequisite to all solver applications.

Lesson 3 • Stochastic Optimisation Basics

Incorporates uncertainty in demand and renewable output into optimisation models. Prepares students for probabilistic planning in advanced chapters.

Lesson 4 • Linear and Nonlinear Programming

Applies LP and NLP methods to dispatch and scheduling problems. Distinguishes convex from non-convex solution landscapes.

Lesson 1 • Unit Commitment Problem Setup

Formulates the 24-hour scheduling problem with startup costs and minimum run times. Extends economic dispatch to binary on/off decisions.

Lesson 2 • Market-Based Dispatch Mechanisms

Models energy market clearing, locational marginal pricing, and ancillary services. Connects technical dispatch outcomes to economic market signals.

Lesson 3 • Renewable Integration in Dispatch

Incorporates variable renewable output and curtailment options into dispatch models. Addresses flexibility requirements introduced by intermittent sources.

Lesson 4 • Economic Dispatch Fundamentals

Minimises total generation cost subject to load balance and generator limits. Establishes the core dispatch problem solved in power system operations.

Lesson 1 • Multi-Service Storage Stacking

Optimises storage providing simultaneous energy arbitrage, frequency regulation, and capacity services. Addresses revenue stacking and service conflict resolution.

Lesson 2 • Storage Dispatch Optimisation

Formulates charge/discharge scheduling as an LP or MIP problem over a planning horizon. Maximises arbitrage value whilst respecting state-of-charge limits.

Lesson 3 • Storage Sizing and Siting

Determines optimal capacity and location of storage within a network using investment models. Balances capital cost against operational savings.

Lesson 4 • Storage in Microgrid Contexts

Applies storage optimisation to islanded and grid-connected microgrids with high renewable penetration. Integrates storage dispatch with microgrid energy management.

Lesson 1 • Multi-Energy System Architecture

Maps energy hubs connecting electricity, heat, and gas flows through shared converters. Establishes the structural framework for integrated simulation.

Lesson 2 • Combined Heat and Power Modelling

Models CHP units with feasible operating regions and heat-to-power ratios. Demonstrates efficiency gains from simultaneous heat and power dispatch.

Lesson 3 • Gas Network Integration

Incorporates gas flow equations and compressor constraints into the integrated model. Captures gas-electricity interdependencies in co-optimisation.

Lesson 4 • Co-Optimisation Formulation

Formulates a single optimisation problem spanning electricity, heat, and gas simultaneously. Quantifies cost and emission benefits over siloed optimisation.

Lesson 1 • Decarbonisation Pathway Analysis

Models emission reduction trajectories using carbon budgets and technology transition constraints. Quantifies the cost of achieving net-zero targets.

Lesson 2 • Long-Term Capacity Expansion Planning

Formulates investment decisions for generation and network assets over multi-year horizons. Balances capital expenditure against future operational costs.

Lesson 3 • Metaheuristic Optimisation Methods

Applies genetic algorithms, particle swarm, and simulated annealing to non-convex energy problems. Evaluates convergence and solution diversity.

Lesson 4 • Robust and Interval Optimisation

Designs solutions that remain feasible under worst-case uncertainty realisations. Extends stochastic methods from Chapter 4 to robust counterpart formulations.