A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems

• MOO framework for the integration of renewable DG into an electric power network.
• Modelling of multiple uncertain operational inputs and propagation by MCS-OPF.
• Integration of the conditional-value-at-risk deviation measure of uncertainty.
• MOO strategy aimed at controlling expected performance and associated uncertainty.
• Conjoint integration of risk into the expected performance – uncertainty trade-off.

We introduce a MOO (multi-objective optimization) framework for the integration of renewable DG (distributed generation) into electric power networks. The framework searches for the optimal size and location of different DG technologies, taking into account uncertainties related to primary renewable resources availability, components failures, power demands and bulk-power supply. A non-sequential MCS-OPF (Monte Carlo simulation and optimal power flow) computational model is developed to emulate the network operation by generating random scenarios from the diverse sources of uncertainty, and assess the system performance in terms of CG (global cost). To measure uncertainty in the system performance, we introduce the DCVaR (conditional value-at-risk deviation) which, due to its axiomatic relation to the CVaR (conditional value-at-risk), allows the conjoint control of risk. A MOO strategy can, then, be adopted for the concurrent minimization of the ECG (expected global cost) and the associated deviation DCVaR(CG). In our work this is operatively implemented by a heuristic search engine based on differential evolution (MOO-DE). An example of application of the proposed framework is given with regards to the IEEE 30 bus test system, where in DCVaR is shown capable of enabling and controlling tradeoffs between optimal expected economic performance, uncertainty and risk.