Demand-side management (DSM) is a process by which the user demand patterns are modified to meet certain desired objectives. Traditionally, DSM was utility-driven, but with an increase in the integration of renewable sources and privacy-conscious consumers, it also becomes a “consumer-driven" process. Promising theoretical studies have shown that privacy can be achieved by shaping the user demand using an energy storage system (ESS). In this paper, we present a framework for utility-driven DSM while considering the user privacy and the ESS operational cost due to its energy losses and capacity degradation. We propose an ESS model using a circuit-based and data-driven approach that can be used to capture the ESS characteristics in control strategy designs. We measure privacy leakage using the Bayesian risk of a hypothesis testing adversary and present a novel recursive algorithm to compute the optimal privacy control strategy. Further, we design an energy-flow control strategy that achieves the Pareto-optimal trade-off between privacy leakage, deviation of demand from a DSM target profile, and the ESS cost. With numerical experiments using real household data and an emulated lithium-ion battery, we show that the desired level of privacy and demand shaping performance can be achieved while reducing the ESS degradation.