By signing the Paris Climate Agreement, The Netherlands has committed itself to curtail its CO2- emissions in order to keep global warming well below 2°C above pre-industrial levels. Pivotal to achieve these targets is the phase-out of CO2-intensive electricity generation technologies and investments in renewables. Current investments aiming to decarbonise the electricity system are predominantly allocated to solar PV and off- and onshore wind energy. However, this trajectory might change as the development of Dynamic Tidal power (DTP) might soon enable The Netherlands to harvest the North Sea’s currently unutilised tidal currents to generate clean electricity. DTP uses a 30-70 kilometre long dam perpendicular to the coast to capture the North Sea’s tidal currents. The alternating currents that proceed parallel to the coast create a hydraulic head over the dam, which turbines in the dam convert into electricity. However, the variable availability of solar irradiation, wind, and tidal currents makes it increasingly complex and costly to match electricity supply and demand as their shares in the electricity mix increase. To ensure electricity security, freely dispatchable energy generators (e.g. natural gas, biomass or coal turbines) are deployed to cover the electricity load that could not be met by the variable renewable energy (VRE) generators. However, due to the fuel used to generate electricity, dispatchable energy sources tend to emit CO2 and bear higher marginal energy generation costs than VRES. To address this problem, the portfolio shares of solar PV, offshore wind, onshore wind and DTP that minimise the need for dispatchable backup capacity and the energy generation costs were computed in this study. Due to its novelty, special attention was paid to the effect of DTP on a VRE system’s ability to efficiently match supply and demand. In order to achieve these objectives, a novel application of the Modern Portfolio Theory (MPT) was used. The MPT originates from the stock market but is often used to comprise VRE portfolios that maximise the electricity output. However, as the aim was to minimise the residual load, demand-variability was introduced into the MPT. Existing literature had not covered this topic. Hence, by assessing to what extent including demand-variability in the MPT affects the selection of efficient VRE portfolios, this study filled a gap in the literature and serves as a starting point for future research into the application of the MPT. In total, three different electricity demand scenarios were optimised; the contemporary load profile (1), increased peak loads due to an extreme penetration of electric vehicles (EV) and electric heat pumps (HP) (2), and a flat load profile due to an extreme penetration of demand-responsive measures (3). The most efficient portfolio shares for each demand scenario were found by computing how 35GW should be distributed among solar PV, offshore wind, onshore wind and DTP in order to minimise the need for backup capacity and energy generation costs. The results of this study indicate that regardless of the demand profile, The Netherlands’ VRE system would be most cost-efficient in meeting demand when comprised of approximately 75% DTP and 25% offshore wind. However, only under the condition that the DTP-dams are geographically dispersed. Geographically dispersed DTP-dams, for example, located in Zeeland (south of The Netherlands) and off the coast of Texel (north of The Netherlands), cancels out the volatility in the electricity output caused by high and low tide. This reduces a VRE system’s volatility in electricity output and stabilises the electricity output from the dispatchable backup system, which minimises the system’s electricity generation costs. However, in terms of the amount of backup capacity required, it was found that a system comprised entirely of DTP with an integrated battery storage system would be even more efficient. The battery storage system eliminates all variability in the electricity output from DTP, which minimises the need for dispatchable backup capacity. However, as battery storage costs are significant (approx. 100,000 €/MWh), it is unlikely that a DTP-dam with a storage system large enough to flatten its electricity output is economically viable under the current market circumstances. This might change when DTP is combined with other storage systems or if battery storage costs reduce due to learning-effect, economies of scale or technical innovations. As DTP is still in its initial development phase, it is not certain DTP will successfully penetrate the power generation market. If DTP fails to enter the market, the current VRE system (61% solar PV, 8% offshore wind and 31% onshore wind) is fairly cost-efficient. However, if the aim is to reduce the amount of dispatchable backup capacity required to ensure energy security, future investments should be allocated to offshore wind. The overarching conclusion is that there is no unequivocal VRE portfolio that is most efficient to meet demand as it depends on the perspective taken (required backup capacity versus electricity generation costs). However, this study shows that the current VRE system benefits from the inclusion of DTP, both in terms of the required amount of backup capacity and the system’s energy generation costs. Only if DTP is combined with a large battery storage system to flatten its electricity output do the system’s energy generation costs surpass the costs of the current VRE system. In regards to the effect of including demand-variability in the MPT, it was found that demand variability has a limited effect on the selection of efficient VRE portfolio shares. Only in a scenario (2) with increased peak loads did the share of offshore wind slightly increase. This is due to the fact that the peaks in electricity supply from offshore wind coincide with the peaks in demand from electric HP (winter).